Assessing Explainability in Deep Learning for Medical Image Analysis
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The document discusses the evaluation of explainable deep learning techniques in medical image analysis, focusing on saliency methods applied to datasets like brain tumor MRI and COVID-19 chest X-rays. It highlights the significance of explainability for clinical adoption and the lack of quantitative analysis in existing methods. Key findings include the effectiveness of specific saliency methods and the proposal of a framework combining qualitative and quantitative assessments to enhance model explainability.
Assessing Explainability in Deep Learning for Medical Image Analysis
- 1. Assessing Explainability in Deep Learning for Medical Image Analysis AI & Data Science Group, Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) December 10th , 2024 Yusuf Brima
- 2. Talk Outline ● Introduction ● Significance of Explainability in Medical Image Analysis ● Experimental Setup ● Evaluation Metrics ● Results ● Key Takeaways 2
- 3. Introduction 3 Colon Pathology, Chest X-Ray, Dermatoscope, Retinal OCT, Chest X-Ray, Fundus Camera, Breast Ultrasound, Blood Cell Microscope, Kidney Cortex Microscope, Abdominal CT Background: The promise of deep learning (DL) in medical image analysis. The challenge of explainability in DL models.
- 4. Research Question How well do state-of-the-art (SoTA) explainable deep learning techniques work for medical image analysis tasks?
- 5. Research Objective 5 To assess image-based saliency methods using objective quantitative analyses Brima, Y., Atemkeng, M. Evaluation of explainability in deep learning for medical image analysis. Springer Nature BioData Mining 17, 18 (2024). https://doi.org/10.1186/s13040-024-00370-4
- 6. Significance of Explainability in Medical Imaging ● Trust and Adoption: ○ Important for clinical adoption of AI in safety-critical settings. ● Research Gap: ○ Existing methods focus on visual inspection without quantitative analysis . ○ Thus, a need for an objective measure of performance of saliency methods. 6
- 7. Experimental Setup ● Datasets: ○ Brain Tumor MRI dataset (w GT annotation). ○ COVID-19 Chest X-ray dataset (w/o GT annotation). ● Models Used: ○ Various CNN architectures (e.g., ResNet, DenseNet, InceptionResNetV2). ● Tasks ○ Multi-task classification ○ Model Explainability 7
- 8. Model Training and Attribution 8 1 2 e.g., ScoreCAM Quantitative and qualitative assessment.
- 9. Saliency Methods 9 ● Gradient-based (e.g., GradCAM, Integrated Gradients). ● Gradient-free (e.g., ScoreCAM). ● Other methods such as : ○ Concept Learning often require extensive annotation to define concepts accurately and risk information leakage ○ LRP provides information about the relevance of input features to the model's output, but it may not reveal all aspects of the model's behavior
- 10. Evaluation Metrics ● Qualitative Assessment: ○ Visual inspection of attribution maps. ● Quantitative Assessment using Performance Information Curves (PICs)1 : ○ AICs to measure the relationship between saliency map intensity and model accuracy. ○ SICs to measure the correlation between the saliency map intensity and the model’s output probabilities (Softmax scores) 10 1 Kapishnikov A, Venugopalan S, Avci B, Wedin B, Terry M, Bolukbasi T. Guided integrated gradients: An adaptive path method for removing noise. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2021. pp. 5050–8.
- 11. 11 Start Make image fully blurred Use saliency method to reintroduce important pixels Compute Entropy Perform Classification Fully Restored Plot AIC End No Yes Shannon Entropy with total intensity levels Start Make image fully blurred Use saliency method to reintroduce important pixels Compute Entropy Perform Classification Fully Restored Plot AIC End No Yes Buffer-size-based Shannon Entropy-based Estimates image entropy by the file size of the image after lossless compression and calculating the buffer length
- 12. Image Blurring and Saliency Assessment for XRAI 12
- 13. Image Blurring and Saliency Assessment for GradCAM 13
- 14. Task-based evaluation 14 Brain MRI Chest X-ray
- 15. Attribution on the Brain MRI 15
- 17. Qualitative Assessment Chest X-ray 17
- 18. Ours on Chest X-ray 18 Existing method
- 19. Key Takeaways ● Enhanced Explainability : The proposed saliency-driven framework effectively combines qualitative and quantitative analyses to assess explanability of deep learning models in medical imaging. ● Critical Role of PICs : PICs provide valuable objective assessment of image-based saliency methods. ● Model-Specific Effectiveness : Saliency methods like ScoreCAM and XRAI were found to be particularly effective. 19
- 20. Limitations and Future Directions ● Limitation ○ 2-D images, however, real-world medical images are often 3-D voxels ○ Further analysis on other medical imaging modalities is also required ● Future directions ○ Alternative entropy estimation approaches due to quantization trade-off with histogram approach ○ 3-D voxel-based saliency methods ○ Clinical translation 20
- 21. Thank you! 21