Medical Imaging AI
ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation
Explainability is essential for AI models, especially in clinical settings where understanding the model's decisions is crucial. Despite their impressive performance, black-box Al models are unsuitable for clinical use if their operations cannot be explained to clinicians. While deep neural networks (DNNs) represent the forefront of model performance, their explanations are often not easily interpreted by humans. On the other hand, hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. However, they often lack the effectiveness of advanced models due to human limitations in feature design. To address this, we propose ExShall-CNN, a novel explainable shallow convolutional neural network for medical image processing. This model improves upon hand-crafted features to maintain human interpretability, ensuring that its decisions are transparent and understandable. We introduce the explainable shallow convolutional neural network (ExShall-CNN), which combines the interpretability of hand-crafted features with the performance of advanced deep convolutional networks like U-Net for medical image segmentation. Built on recent advancements in machine learning, ExShall-CNN incorporates widely used kernels while ensuring transparency, making its decisions visually interpretable by physicians and clinicians. This balanced approach offers both the accuracy of deep learning models and the explainability needed for clinical applications.
Executive Impact Summary
ExShall-CNN offers a balanced approach to medical image segmentation, combining the interpretability of hand-crafted features with the performance benefits of deep learning. It's designed to be visually interpretable by clinicians, addressing a critical need for transparency in AI in clinical settings. While not outperforming U-Net in all metrics, it shows superior generalization over FCNN and significantly fewer parameters, highlighting its efficiency and explainability.
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Shallow CNN DICE Score (Retina Test)
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Shallow CNN DICE Score (ISIC Test)
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Parameters (Shallow CNN)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
73.6%
DICE Score on Retina Blood Vessel Dataset (Testing)
| Model | Num. Parameters | Training DICE (%) | Testing DICE (%) |
|---|---|---|---|
| Fully CNN | 54,304,086 | 77.6 | 72.4 |
| U-Net | 31,043,521 | 77.7 | 79.0 |
| Shallow CNN (ExShall-CNN) | 39,698 | 72.0 | 73.6 |
- ✓ExShall-CNN offers superior generalization compared to FCNN with significantly fewer parameters.
- ✓While U-Net shows higher performance, it has a vastly larger parameter count (order of magnitude higher).
| Model | Num. Parameters | Training DICE (%) | Testing DICE (%) |
|---|---|---|---|
| Fully CNN | 54,304,086 | 90.6 | 77.9 |
| U-Net | 31,043,521 | 71.2 | 81.9 |
| Shallow CNN (ExShall-CNN) | 39,698 | 62.5 | 81.3 |
- ✓ExShall-CNN's performance is comparable to U-Net on the ISIC testing dataset, with far fewer parameters.
- ✓Highlights the model's efficiency and explainability over complex deep learning architectures.
Enterprise Process Flow
Model Decision Made
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Identify Impactful Modules (Eq. 5)
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Visualize Kernel Transformations
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Clinician Interpretation & Validation
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Enhanced Trust in AI
Clinical Applicability of Explainable AI
Introduction: In clinical settings, AI model explainability is paramount for trust and adoption. ExShall-CNN addresses this by making its decisions transparent and visually interpretable.
Challenge: Black-box AI models, despite high accuracy, are often unsuitable for clinical use due to their lack of transparency, making auditing and validation by clinicians difficult.
Solution: ExShall-CNN combines the interpretability of hand-crafted features with the performance of deep learning, using novel activation functions and visual explanation of kernel transformations.
Outcome: Clinicians can visually examine how ExShall-CNN processes images, identifying which features and transformations drive decisions, leading to greater confidence and facilitating clinical validation.
Calculate Your Potential ROI
Estimate the impact ExShall-CNN could have on your operational efficiency and cost savings in medical image processing.
Your Implementation Roadmap
A phased approach to integrating ExShall-CNN into your medical imaging workflow, ensuring explainability and high performance at every step.
Phase 1: Initial Setup & Data Integration
Establish the necessary Python and PyTorch environments. Integrate specific medical imaging datasets (Retina Blood Vessel, ISIC) and prepare for model training and validation.
Phase 2: Model Configuration & Training
Configure ExShall-CNN with specified kernel sizes and activation functions. Train the model using Adam optimizer with an optimal learning rate, focusing on Jaccard and Dice scores.
Phase 3: Explainability Analysis & Validation
Implement the impact scoring method (Equation 5) to identify the most influential modules. Generate visual transformations for key kernels to demonstrate explainability to clinicians.
Phase 4: Comparative Evaluation & Reporting
Compare ExShall-CNN's performance against FCNN and U-Net on both training and testing datasets. Document findings, emphasizing generalization, parameter efficiency, and explainability.
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