AI in Medical Imaging
Concept-based Representation for Feature Explainability in Skin Lesion Classification
Deep learning has been robustly developing and playing an important role in various fields, such as medical image analysis. However, neural networks are still black-boxes thus preventing them from high-stakes applications, especially medicine. This study proposes a methodology to identify learned concepts in the feature space for reasoning network predictions based on similar concepts or samples. A novel approach for feature presentation, based on concept distances, is introduced to evaluate the informative and discriminative levels of learned features. Experiments on the HAM10000 dataset prove the power of reasonability, contributing directly to explainable artificial intelligence in medical image classification.
Executive Impact & Key Findings
Our analysis highlights critical advancements and quantifiable results from the research, demonstrating clear pathways for enterprise value.
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Avg. Accuracy Diff (Baseline vs. Model)
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SVM Accuracy Diff (Baseline vs. Model)
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Total Concepts Discovered
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Training Epochs to Converge
Deep Analysis & Enterprise Applications
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Computer Vision Breakthroughs
This research specifically advances Explainable AI (XAI) in Computer Vision, particularly for medical image classification. By converting complex neural network features into interpretable concepts and quantifiable distances, it addresses the black-box problem. This allows for transparent, reason-based predictions vital for high-stakes applications like dermatology, where understanding why a diagnosis is made is as crucial as the diagnosis itself.
Enterprise Process Flow: Concept-based Explainability
Skin Lesion Classification (ResNet-50)
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Feature Extraction (2D Embeddings)
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Concept Inference (k-Means Clustering)
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Concept-Distance Representation
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Performance Contrasting (Baseline Classifiers)
2.5%
Average Accuracy Difference for High-Quality Features, validating feature informativeness.
Baseline Classifier Performance vs. Original Model
| Model | Train Accuracy | Val Accuracy | Test Accuracy | ΔTest (%) |
|---|---|---|---|---|
| CNN (Original) | 0.9938 | 0.8565 | 0.8465 | N/A |
| Decision Tree | 1.0000 | 0.8020 | 0.7987 | -4.78 |
| Random Forest | 1.0000 | 0.8515 | 0.8312 | -1.53 |
| SVM | 0.9932 | 0.8505 | 0.8382 | -0.83 |
| MLP | 0.9890 | 0.8350 | 0.8216 | -2.49 |
| k-NN | 0.9922 | 0.8495 | 0.8322 | -1.43 |
HAM10000 Skin Lesion Dataset Application
The proposed methodology was rigorously tested on the HAM10000 dataset, a large collection of multi-source dermatoscopic images across seven distinct skin lesion diseases (akiec, bcc, bkl, df, mel, nv, vasc). The results demonstrated that the learned features, when represented by concept distances, are highly informative and discriminative. The low average accuracy differences (within 2.5%) between the baseline classifiers (Decision Tree, Random Forest, SVM, MLP, k-NN) and the original CNN model confirm the quality of the derived features, showcasing the potential for enhanced explainability in medical image diagnosis.
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Your AI Implementation Roadmap
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Phase 1: Discovery & Strategy
Comprehensive assessment of current systems, data infrastructure, and business objectives. Definition of AI use cases, success metrics, and a tailored implementation strategy leveraging explainable AI principles.
Phase 2: Data Preparation & Model Development
Collection, cleaning, and annotation of relevant datasets (e.g., HAM10000 for medical images). Development of custom deep learning models with integrated concept-based explainability mechanisms.
Phase 3: Integration & Validation
Seamless integration of AI models into existing workflows. Rigorous validation of model performance and explainability features with domain experts to ensure accuracy and trust.
Phase 4: Deployment & Optimization
Full-scale deployment of the AI solution. Continuous monitoring, performance optimization, and iterative improvements based on real-world feedback and evolving business needs.
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