RL-ViT-alia (previously "ViTalia") is a novel approach to malaria detection that integrates Vision Transformers (ViT) with advanced AI techniques, including Reinforcement Learning, Chain-of-Thought reasoning, and Retrieval-Augmented Generation. This project aims to create an accurate, interpretable, and reliable system for automated malaria diagnosis from blood smear images.

• Phase 1: Initial development with Vision Transformers for malaria detection, achieving high accuracy in classifying infected vs. uninfected blood cells
• Phase 2: Integration of Chain-of-Thought (CoT), Retrieval-Augmented Generation (RAG), and Proximal Policy Optimization (PPO) to enhance performance and interpretability

• High Accuracy: Achieves 96.7% accuracy on malaria detection, outperforming traditional CNN approaches
• Interpretable Decisions: Generates step-by-step reasoning and visual attention maps to explain diagnostic decisions
• Confidence Calibration: Uses reinforcement learning to calibrate confidence levels, reducing both overconfidence and underconfidence
• Case-Based Reasoning: Retrieves similar cases from a knowledge base to assist in classification and provide context
• Balanced Performance: Optimized to minimize both false positives and false negatives, with particular emphasis on reducing false negatives (missed infections)

The system integrates multiple advanced components:

1. Vision Transformer (ViT) Backbone: Leverages transformer architecture for effective feature extraction from blood cell images
2. Retrieval-Augmented Generation (RAG): Enhances decisions through reference to similar cases in a knowledge base
3. Chain-of-Thought (CoT) Reasoning: Provides step-by-step interpretable reasoning for diagnostic decisions
4. Proximal Policy Optimization (PPO): Fine-tunes the model through reinforcement learning to optimize for a custom reward function that balances accuracy, confidence calibration, and explanation quality

Figure: RL-ViTalia model graphs

Figure: Attention Maps produce by CoT based on cell morphology

The model is trained and evaluated on the "Cell Images for Detecting Malaria" dataset from Kaggle:

• Cell Images for Detecting Malaria

This dataset contains microscopic images of stained blood cells, both infected and uninfected with malaria parasites.

The model provides comprehensive explanations for each diagnosis:

Sample 1:
True Label: Parasitized
Base ViT: Parasitized (confidence: 0.9976)
CoT-PPO: Parasitized (confidence: 0.9648)

Chain-of-Thought Reasoning:
Step 1: The cell boundary was analyzed to assess morphological regularity.
Step 2: Region-level focus revealed potential parasitic inclusions.
Step 3: Attention was paid to the chromatin structure and ring formations.

Conclusion: The cell is likely parasitized with a confidence of 96.5%.
Key infection traits detected include:
- Disrupted membrane boundary
- Chromatin dot visibility
- Parasite-like inclusions within the cytoplasm

Reference Similar Cases:
- Case #1: Parasitized (similarity: 1.5%)
- Case #2: Parasitized (similarity: -3.6%)
- Case #3: Parasitized (similarity: -9.1%)

Figure: Visualization of Chain-of-Thought Reasoning

1. Improved Accuracy: The combination of techniques achieves higher accuracy than traditional CNN or ViT models alone
2. Interpretability: Chain-of-Thought reasoning provides step-by-step explanations crucial for medical applications
3. Knowledge Integration: Retrieval-Augmented Generation allows comparison with known examples, enhancing decision quality
4. Confidence Calibration: PPO reinforcement learning helps calibrate the model's confidence, particularly important for medical diagnosis
5. Visual Evidence: Attention maps provide visual evidence of the model's focus areas, enhancing transparency
6. Adaptability: The reinforcement learning framework can be adjusted to emphasize different performance aspects without retraining

• Efficient Implementation: Optimizing the architecture for deployment in resource-constrained environments
• Multi-modal Integration: Incorporating additional data sources such as patient history and symptoms
• Improved Explanations: Developing more sophisticated explanation generation techniques
• Online Learning: Implementing mechanisms for continuous improvement from new cases and expert feedback
• Extension to Other Diseases: Adapting the approach to other medical imaging tasks
• Human-AI Collaboration: Developing interfaces for effective collaboration between healthcare professionals and the AI system