Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning

Niccolò Marini, Stefano Marchesin, Lluis Borras Ferris, Simon Püttmann, Marek Wodzinski, Riccardo Fratti, Damian Podareanu, Alessandro Caputo, Svetla Boytcheva, Simona Vatrano, Filippo Fraggetta, Iris Nagtegaal, Gianmaria Silvello, Manfredo Atzori, Henning Müller·June 20, 2024

Summary

This collection of studies investigates the use of automatic labels in training deep learning models for biomedical image classification, specifically in histopathology. Researchers have found that when the noise rate of automatic labels is below 10-20%, models can achieve competitive performance with manual labels, particularly when using tools like the Semantic Knowledge Extractor Tool (SKET) which generates labels with low noise rates. Studies have employed various architectures, such as CNNs, ViT, and MIL backbones, and have analyzed tasks like celiac disease, lung cancer, and colon cancer classification. The research suggests that automatic labels can be a viable alternative to manual annotations, reducing the need for expert input and enabling more efficient use of data in large-scale applications. However, performance degradation is observed with increasing levels of label noise, emphasizing the importance of high-quality automatic label generation. Some studies also highlight the potential of unsupervised methods and data augmentation techniques to enhance model performance. Overall, the findings indicate that automatic labels can be a valuable resource for biomedical image analysis, provided the label quality is sufficient.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of reducing the time and effort required for data annotation in the biomedical domain by exploring the effectiveness of automatic labels compared to manual labels in classifying biomedical images using deep learning models . This problem is not entirely new, but the paper contributes by demonstrating that automatic labels can be as effective as manual labels, thereby saving up to 99.99% of the time needed for data annotation . The research focuses on the application of weak automatic labels to train computer algorithms for the analysis of biomedical data, highlighting the potential of exploiting vast amounts of unlabeled data to build more accurate and robust models .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that automatic labels are as effective as manual labels in the classification of biomedical images using deep learning architectures . The comparison involves experiments in both controlled and real-case scenarios, evaluating the performance of models trained with automatic and manual labels on the classification of WSIs related to celiac disease, lung cancer, and colon cancer . The study aims to demonstrate that meaningful concepts extracted from pathology reports using tools like SKET can serve as weak labels, reducing the need for human intervention in labeling data and significantly saving time in the data annotation process .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" introduces several innovative ideas, methods, and models in the field of biomedical image classification :

  1. Automatic Labeling with SKET Algorithm: The paper proposes the use of the SKET algorithm to automatically generate weak binary labels for computer vision architectures in the classification of Whole Slide Images (WSIs). These automatic labels are extracted from meaningful concepts in pathology reports, eliminating the need for manual labeling and significantly reducing the time required to collect labels .

  2. Evaluation of Noisy Labels: The study evaluates the impact of noisy weak labels on the binary classification of WSIs. By training models with both manual and noisy labels perturbated at different percentages of noise, the paper assesses the effectiveness of automatic labels in the presence of label noise .

  3. Performance Comparison of Automatic and Manual Labels: The research compares the performance of models trained with automatic labels generated by SKET and manual labels provided by medical experts. The results show that the automatic labels are as effective as manual labels in binary classification scenarios, with a slight decrease in performance but not statistically significant when the percentage of mislabeled data is between 2% and 5% .

  4. Hyperparameter Optimization: The paper discusses the optimization of hyperparameters for Convolutional Neural Network (CNN) and Vision Transformer (ViT) models using a grid search algorithm. Parameters such as batch size, optimizers, number of epochs, learning rate, and decay rate are tuned to achieve the lowest loss function in the classification of WSIs .

  5. Distillation Mechanism: The study introduces a distillation mechanism involving two networks, a teacher, and a student model, trained with augmented versions of input samples. The student model is trained with a cropped version of the teacher inputs, enhancing the learning process .

  6. Image Data Augmentation: The research utilizes the Albumentations library to apply data augmentation techniques to input images, including random rotations, flipping, and color augmentation. These operations aim to enhance the robustness and generalization of the models .

Overall, the paper presents a comprehensive analysis of the effectiveness of automatic labels compared to manual labels in biomedical image classification, introducing novel approaches to label generation, model training, and performance evaluation in the context of deep learning algorithms applied to medical imaging tasks. The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" introduces several characteristics and advantages compared to previous methods in the field of biomedical image classification:

  1. Automatic Labeling with SKET Algorithm: The paper proposes the use of the SKET algorithm to automatically generate weak binary labels for computer vision architectures in the classification of Whole Slide Images (WSIs). This approach eliminates the need for manual labeling, significantly reducing the time required to collect labels .

  2. Evaluation of Label Noise: The study evaluates the impact of noisy weak labels on the binary classification of WSIs. By training models with both manual and noisy labels perturbated at different percentages of noise, the paper assesses the effectiveness of automatic labels in the presence of label noise .

  3. Performance Comparison with Manual Labels: The research compares the performance of models trained with automatic labels generated by SKET and manual labels provided by medical experts. The results show that the automatic labels are as effective as manual labels in binary classification scenarios, with a slight decrease in performance but not statistically significant when the percentage of mislabeled data is between 2% and 5% .

  4. Hyperparameter Optimization: The paper discusses the optimization of hyperparameters for Convolutional Neural Network (CNN) and Vision Transformer (ViT) models using a grid search algorithm. Parameters such as batch size, optimizers, number of epochs, learning rate, and decay rate are tuned to achieve the lowest loss function in the classification of WSIs .

  5. Distillation Mechanism: The study introduces a distillation mechanism involving two networks, a teacher, and a student model, trained with augmented versions of input samples. This mechanism enhances the learning process by training the student model with a cropped version of the teacher inputs, improving model performance .

  6. Image Data Augmentation: The research utilizes the Albumentations library to apply data augmentation techniques to input images, enhancing the robustness and generalization of the models. Operations such as random rotations, flipping, and color augmentation are applied to improve model performance .

Overall, the paper presents innovative approaches in automatic labeling, model training, hyperparameter optimization, and performance evaluation, showcasing the effectiveness of automatic labels in biomedical image classification tasks compared to manual labeling methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of biomedical image classification with deep learning. The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" discusses the comparison of deep learning architectures trained with automatic and manual labels . Noteworthy researchers in this field include Lu et al., Shao et al., and Chen et al., who developed the deep learning architectures CLAM, transMIL, and Vision Transformer (ViT) respectively .

The key solution mentioned in the paper involves the use of the Semantic Knowledge Extractor Tool (SKET) to extract meaningful concepts from reports, which are then used as weak labels for training deep learning models . SKET is effective in extracting concepts without human intervention, significantly reducing the time needed to collect labels and annotate large amounts of data for training models .


How were the experiments in the paper designed?

The experiments in the paper were designed with two sets of experiments: a controlled scenario and a real-case scenario .

  • In the controlled scenario, manual labels were randomly perturbed with different percentages of noise to simulate the output of an algorithm generating automatic labels. This perturbation involved modifications in the labels based on the specific use case, such as flipping labels for celiac disease, assigning different classes for lung cancer, and flipping classes for colon cancer .
  • In the real-case scenario, the Semantic Knowledge Extractor Tool (SKET) was utilized to extract meaningful concepts from reports, which were then used as weak labels for the corresponding samples . These experiments involved three tissue use cases: celiac disease, lung cancer, and colon cancer, with over 10,000 Whole Slide Images (WSIs) used to train three deep learning architectures: CLAM, transMIL, and Vision Transformer (ViT) .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Regarding the openness of the code, the information about whether the code is open source is not specified in the context provided.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study compares the classification performance of deep learning architectures trained with automatic and manual labels in the context of biomedical image classification . The experiments involve controlled scenarios where manual labels are perturbed to simulate automatic labels and real-case scenarios where automatic labels are generated from pathology reports using the Semantic Knowledge Extractor Tool (SKET) . The results show that the performance of models trained with automatic labels is comparable to those trained with manual labels, with only a slight decrease in performance that is not statistically significant . This indicates that automatic labels can be as effective as manual labels in biomedical image classification tasks, supporting the hypothesis that automatic labeling methods can yield comparable results to manual labeling processes .


What are the contributions of this paper?

The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" makes the following contributions:

  • It includes a comparison of deep learning architectures trained with automatic and manual labels for the classification of WSIs .
  • The comparison involves two sets of experiments: a controlled scenario where manual labels are perturbed with noise, and a real-case scenario where the Semantic Knowledge Extractor Tool (SKET) is used to extract meaningful concepts from reports as weak labels .
  • The study evaluates the performance of SKET on extracting meaningful concepts from pathology reports for celiac disease, lung cancer, and colon cancer, achieving high F1-scores .
  • The paper demonstrates that the algorithm SKET can efficiently mine unlabeled datasets and annotate large amounts of data, significantly reducing the time needed compared to human experts .
  • It highlights the effectiveness of automatic labels in a binary classification scenario, showing that the performance is similar to that obtained with manual labels, especially when a low percentage of training samples are wrongly annotated .

What work can be continued in depth?

Further research in the field of biomedical image classification with deep learning can be expanded in several areas:

  • Exploring Noisy Labels: Future studies can delve deeper into the impact of noisy labels on model performance. The research indicates that 10% noise in labels can lead to competitive model training . Investigating different levels of noise and their effects on classification accuracy can provide valuable insights for improving model robustness.
  • Comparative Analysis: Conducting more comparative analyses between models trained with automatic and manual labels can enhance understanding. The study suggests that automatic labels are as effective as manual labels in biomedical image classification . Further investigations can focus on specific scenarios or datasets to validate this finding across different contexts.
  • Algorithm Development: Developing algorithms for generating automatic labels that meet specific criteria for effective model training is an area for advancement. The Semantic Knowledge Extractor Tool (SKET) algorithm has shown promising results in generating automatic labels with low noise percentages . Enhancing such algorithms to optimize label quality and training efficiency can be a valuable direction for future research.
  • Application in Clinical Settings: Extending the research to explore the practical application of automatic labels in clinical settings can be beneficial. Understanding how these automated labeling methods can be integrated into real-world pathology workflows and their impact on diagnostic accuracy and efficiency is crucial for translating research findings into practical use .
  • Validation Studies: Conducting validation studies on larger and more diverse datasets can provide more comprehensive insights into the generalizability and scalability of models trained with automatic labels. Validating the findings on a broader scale can strengthen the evidence supporting the effectiveness of automatic labels in biomedical image classification .

Tables

8

Introduction
Background
Emergence of deep learning in histopathology
Challenges with manual annotation in biomedical imaging
Objective
To evaluate the effectiveness of automatic labels in model training
To explore the role of tools like SKET in noise reduction
To analyze the impact of different architectures and tasks
Methodology
Data Collection
Selection of datasets with varying noise rates
Usage of SKET and other automatic labeling tools
Data Preprocessing
Cleaning and validation of automatic labels
Integration of high-quality and noisy labels
Model Architectures
Convolutional Neural Networks (CNNs)
Vision Transformer (ViT) models
Multiple Instance Learning (MIL) backbones
Performance Analysis
Evaluation with low noise rates (10-20%)
Impact of increasing label noise on model performance
Unsupervised Learning and Data Augmentation
Exploration of unsupervised techniques
Role in enhancing model robustness
Efficiency and Scalability
Comparison with manual annotation in terms of time and resource usage
Case Studies
Celiac disease classification
Lung cancer detection
Colon cancer classification
Discussion
Limitations and potential improvements in automatic labeling
Future directions for noise reduction strategies
Real-world implications and large-scale application potential
Conclusion
Summary of key findings on automatic labels in biomedical image analysis
The role of label quality in model performance
Implications for the future of deep learning in histopathology with automatic labels.
Basic info
papers
image and video processing
computer vision and pattern recognition
machine learning
artificial intelligence
Advanced features
Insights
What types of deep learning models are used in the studies for biomedical image classification?
At what noise rate do researchers find models can achieve competitive performance with manual labels using automatic ones?
What are some specific tasks in biomedical image analysis that have been studied using automatic labels?
Which tool is mentioned for generating labels with low noise rates that contribute to better model performance?

Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning

Niccolò Marini, Stefano Marchesin, Lluis Borras Ferris, Simon Püttmann, Marek Wodzinski, Riccardo Fratti, Damian Podareanu, Alessandro Caputo, Svetla Boytcheva, Simona Vatrano, Filippo Fraggetta, Iris Nagtegaal, Gianmaria Silvello, Manfredo Atzori, Henning Müller·June 20, 2024

Summary

This collection of studies investigates the use of automatic labels in training deep learning models for biomedical image classification, specifically in histopathology. Researchers have found that when the noise rate of automatic labels is below 10-20%, models can achieve competitive performance with manual labels, particularly when using tools like the Semantic Knowledge Extractor Tool (SKET) which generates labels with low noise rates. Studies have employed various architectures, such as CNNs, ViT, and MIL backbones, and have analyzed tasks like celiac disease, lung cancer, and colon cancer classification. The research suggests that automatic labels can be a viable alternative to manual annotations, reducing the need for expert input and enabling more efficient use of data in large-scale applications. However, performance degradation is observed with increasing levels of label noise, emphasizing the importance of high-quality automatic label generation. Some studies also highlight the potential of unsupervised methods and data augmentation techniques to enhance model performance. Overall, the findings indicate that automatic labels can be a valuable resource for biomedical image analysis, provided the label quality is sufficient.
Mind map
Comparison with manual annotation in terms of time and resource usage
Role in enhancing model robustness
Exploration of unsupervised techniques
Impact of increasing label noise on model performance
Evaluation with low noise rates (10-20%)
Multiple Instance Learning (MIL) backbones
Vision Transformer (ViT) models
Convolutional Neural Networks (CNNs)
Integration of high-quality and noisy labels
Cleaning and validation of automatic labels
Usage of SKET and other automatic labeling tools
Selection of datasets with varying noise rates
To analyze the impact of different architectures and tasks
To explore the role of tools like SKET in noise reduction
To evaluate the effectiveness of automatic labels in model training
Challenges with manual annotation in biomedical imaging
Emergence of deep learning in histopathology
Implications for the future of deep learning in histopathology with automatic labels.
The role of label quality in model performance
Summary of key findings on automatic labels in biomedical image analysis
Real-world implications and large-scale application potential
Future directions for noise reduction strategies
Limitations and potential improvements in automatic labeling
Colon cancer classification
Lung cancer detection
Celiac disease classification
Efficiency and Scalability
Unsupervised Learning and Data Augmentation
Performance Analysis
Model Architectures
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Case Studies
Methodology
Introduction
Outline
Introduction
Background
Emergence of deep learning in histopathology
Challenges with manual annotation in biomedical imaging
Objective
To evaluate the effectiveness of automatic labels in model training
To explore the role of tools like SKET in noise reduction
To analyze the impact of different architectures and tasks
Methodology
Data Collection
Selection of datasets with varying noise rates
Usage of SKET and other automatic labeling tools
Data Preprocessing
Cleaning and validation of automatic labels
Integration of high-quality and noisy labels
Model Architectures
Convolutional Neural Networks (CNNs)
Vision Transformer (ViT) models
Multiple Instance Learning (MIL) backbones
Performance Analysis
Evaluation with low noise rates (10-20%)
Impact of increasing label noise on model performance
Unsupervised Learning and Data Augmentation
Exploration of unsupervised techniques
Role in enhancing model robustness
Efficiency and Scalability
Comparison with manual annotation in terms of time and resource usage
Case Studies
Celiac disease classification
Lung cancer detection
Colon cancer classification
Discussion
Limitations and potential improvements in automatic labeling
Future directions for noise reduction strategies
Real-world implications and large-scale application potential
Conclusion
Summary of key findings on automatic labels in biomedical image analysis
The role of label quality in model performance
Implications for the future of deep learning in histopathology with automatic labels.
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of reducing the time and effort required for data annotation in the biomedical domain by exploring the effectiveness of automatic labels compared to manual labels in classifying biomedical images using deep learning models . This problem is not entirely new, but the paper contributes by demonstrating that automatic labels can be as effective as manual labels, thereby saving up to 99.99% of the time needed for data annotation . The research focuses on the application of weak automatic labels to train computer algorithms for the analysis of biomedical data, highlighting the potential of exploiting vast amounts of unlabeled data to build more accurate and robust models .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that automatic labels are as effective as manual labels in the classification of biomedical images using deep learning architectures . The comparison involves experiments in both controlled and real-case scenarios, evaluating the performance of models trained with automatic and manual labels on the classification of WSIs related to celiac disease, lung cancer, and colon cancer . The study aims to demonstrate that meaningful concepts extracted from pathology reports using tools like SKET can serve as weak labels, reducing the need for human intervention in labeling data and significantly saving time in the data annotation process .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" introduces several innovative ideas, methods, and models in the field of biomedical image classification :

  1. Automatic Labeling with SKET Algorithm: The paper proposes the use of the SKET algorithm to automatically generate weak binary labels for computer vision architectures in the classification of Whole Slide Images (WSIs). These automatic labels are extracted from meaningful concepts in pathology reports, eliminating the need for manual labeling and significantly reducing the time required to collect labels .

  2. Evaluation of Noisy Labels: The study evaluates the impact of noisy weak labels on the binary classification of WSIs. By training models with both manual and noisy labels perturbated at different percentages of noise, the paper assesses the effectiveness of automatic labels in the presence of label noise .

  3. Performance Comparison of Automatic and Manual Labels: The research compares the performance of models trained with automatic labels generated by SKET and manual labels provided by medical experts. The results show that the automatic labels are as effective as manual labels in binary classification scenarios, with a slight decrease in performance but not statistically significant when the percentage of mislabeled data is between 2% and 5% .

  4. Hyperparameter Optimization: The paper discusses the optimization of hyperparameters for Convolutional Neural Network (CNN) and Vision Transformer (ViT) models using a grid search algorithm. Parameters such as batch size, optimizers, number of epochs, learning rate, and decay rate are tuned to achieve the lowest loss function in the classification of WSIs .

  5. Distillation Mechanism: The study introduces a distillation mechanism involving two networks, a teacher, and a student model, trained with augmented versions of input samples. The student model is trained with a cropped version of the teacher inputs, enhancing the learning process .

  6. Image Data Augmentation: The research utilizes the Albumentations library to apply data augmentation techniques to input images, including random rotations, flipping, and color augmentation. These operations aim to enhance the robustness and generalization of the models .

Overall, the paper presents a comprehensive analysis of the effectiveness of automatic labels compared to manual labels in biomedical image classification, introducing novel approaches to label generation, model training, and performance evaluation in the context of deep learning algorithms applied to medical imaging tasks. The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" introduces several characteristics and advantages compared to previous methods in the field of biomedical image classification:

  1. Automatic Labeling with SKET Algorithm: The paper proposes the use of the SKET algorithm to automatically generate weak binary labels for computer vision architectures in the classification of Whole Slide Images (WSIs). This approach eliminates the need for manual labeling, significantly reducing the time required to collect labels .

  2. Evaluation of Label Noise: The study evaluates the impact of noisy weak labels on the binary classification of WSIs. By training models with both manual and noisy labels perturbated at different percentages of noise, the paper assesses the effectiveness of automatic labels in the presence of label noise .

  3. Performance Comparison with Manual Labels: The research compares the performance of models trained with automatic labels generated by SKET and manual labels provided by medical experts. The results show that the automatic labels are as effective as manual labels in binary classification scenarios, with a slight decrease in performance but not statistically significant when the percentage of mislabeled data is between 2% and 5% .

  4. Hyperparameter Optimization: The paper discusses the optimization of hyperparameters for Convolutional Neural Network (CNN) and Vision Transformer (ViT) models using a grid search algorithm. Parameters such as batch size, optimizers, number of epochs, learning rate, and decay rate are tuned to achieve the lowest loss function in the classification of WSIs .

  5. Distillation Mechanism: The study introduces a distillation mechanism involving two networks, a teacher, and a student model, trained with augmented versions of input samples. This mechanism enhances the learning process by training the student model with a cropped version of the teacher inputs, improving model performance .

  6. Image Data Augmentation: The research utilizes the Albumentations library to apply data augmentation techniques to input images, enhancing the robustness and generalization of the models. Operations such as random rotations, flipping, and color augmentation are applied to improve model performance .

Overall, the paper presents innovative approaches in automatic labeling, model training, hyperparameter optimization, and performance evaluation, showcasing the effectiveness of automatic labels in biomedical image classification tasks compared to manual labeling methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of biomedical image classification with deep learning. The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" discusses the comparison of deep learning architectures trained with automatic and manual labels . Noteworthy researchers in this field include Lu et al., Shao et al., and Chen et al., who developed the deep learning architectures CLAM, transMIL, and Vision Transformer (ViT) respectively .

The key solution mentioned in the paper involves the use of the Semantic Knowledge Extractor Tool (SKET) to extract meaningful concepts from reports, which are then used as weak labels for training deep learning models . SKET is effective in extracting concepts without human intervention, significantly reducing the time needed to collect labels and annotate large amounts of data for training models .


How were the experiments in the paper designed?

The experiments in the paper were designed with two sets of experiments: a controlled scenario and a real-case scenario .

  • In the controlled scenario, manual labels were randomly perturbed with different percentages of noise to simulate the output of an algorithm generating automatic labels. This perturbation involved modifications in the labels based on the specific use case, such as flipping labels for celiac disease, assigning different classes for lung cancer, and flipping classes for colon cancer .
  • In the real-case scenario, the Semantic Knowledge Extractor Tool (SKET) was utilized to extract meaningful concepts from reports, which were then used as weak labels for the corresponding samples . These experiments involved three tissue use cases: celiac disease, lung cancer, and colon cancer, with over 10,000 Whole Slide Images (WSIs) used to train three deep learning architectures: CLAM, transMIL, and Vision Transformer (ViT) .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Regarding the openness of the code, the information about whether the code is open source is not specified in the context provided.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study compares the classification performance of deep learning architectures trained with automatic and manual labels in the context of biomedical image classification . The experiments involve controlled scenarios where manual labels are perturbed to simulate automatic labels and real-case scenarios where automatic labels are generated from pathology reports using the Semantic Knowledge Extractor Tool (SKET) . The results show that the performance of models trained with automatic labels is comparable to those trained with manual labels, with only a slight decrease in performance that is not statistically significant . This indicates that automatic labels can be as effective as manual labels in biomedical image classification tasks, supporting the hypothesis that automatic labeling methods can yield comparable results to manual labeling processes .


What are the contributions of this paper?

The paper "Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning" makes the following contributions:

  • It includes a comparison of deep learning architectures trained with automatic and manual labels for the classification of WSIs .
  • The comparison involves two sets of experiments: a controlled scenario where manual labels are perturbed with noise, and a real-case scenario where the Semantic Knowledge Extractor Tool (SKET) is used to extract meaningful concepts from reports as weak labels .
  • The study evaluates the performance of SKET on extracting meaningful concepts from pathology reports for celiac disease, lung cancer, and colon cancer, achieving high F1-scores .
  • The paper demonstrates that the algorithm SKET can efficiently mine unlabeled datasets and annotate large amounts of data, significantly reducing the time needed compared to human experts .
  • It highlights the effectiveness of automatic labels in a binary classification scenario, showing that the performance is similar to that obtained with manual labels, especially when a low percentage of training samples are wrongly annotated .

What work can be continued in depth?

Further research in the field of biomedical image classification with deep learning can be expanded in several areas:

  • Exploring Noisy Labels: Future studies can delve deeper into the impact of noisy labels on model performance. The research indicates that 10% noise in labels can lead to competitive model training . Investigating different levels of noise and their effects on classification accuracy can provide valuable insights for improving model robustness.
  • Comparative Analysis: Conducting more comparative analyses between models trained with automatic and manual labels can enhance understanding. The study suggests that automatic labels are as effective as manual labels in biomedical image classification . Further investigations can focus on specific scenarios or datasets to validate this finding across different contexts.
  • Algorithm Development: Developing algorithms for generating automatic labels that meet specific criteria for effective model training is an area for advancement. The Semantic Knowledge Extractor Tool (SKET) algorithm has shown promising results in generating automatic labels with low noise percentages . Enhancing such algorithms to optimize label quality and training efficiency can be a valuable direction for future research.
  • Application in Clinical Settings: Extending the research to explore the practical application of automatic labels in clinical settings can be beneficial. Understanding how these automated labeling methods can be integrated into real-world pathology workflows and their impact on diagnostic accuracy and efficiency is crucial for translating research findings into practical use .
  • Validation Studies: Conducting validation studies on larger and more diverse datasets can provide more comprehensive insights into the generalizability and scalability of models trained with automatic labels. Validating the findings on a broader scale can strengthen the evidence supporting the effectiveness of automatic labels in biomedical image classification .
Tables
8
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