qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization
Introduction of qZACH-ViT, a quantization-aware, zero-token, position-free Vision Transformer designed for compact medical image classification with intrinsic interpretability. Development of Recursive Attribution-Stabilized Optimization (RASO), a novel training procedure that aligns classification and attribution gradients to enhance explanation stability. Empirical validation on seven MedMNIST datasets demonstrates that INT8 quantization preserves high prediction accuracy (99.9751% agreement)
Analysis
TL;DR
- Introduction of qZACH-ViT, a quantization-aware, zero-token, position-free Vision Transformer designed for compact medical image classification with intrinsic interpretability.
- Development of Recursive Attribution-Stabilized Optimization (RASO), a novel training procedure that aligns classification and attribution gradients to enhance explanation stability.
- Empirical validation on seven MedMNIST datasets demonstrates that INT8 quantization preserves high prediction accuracy (99.9751% agreement) while significantly reducing model size and latency.
- RASO outperforms standard Adam optimization in reducing sufficiency error and improving input-noise stability for intrinsic explanations, establishing a new standard for deployable XAI models.
Why It Matters
This research addresses the critical trade-off between model efficiency and interpretability in medical AI, where both computational constraints and clinical trust are paramount. By demonstrating that quantized models can maintain near-perfect fidelity to their full-precision counterparts while offering stable, intrinsic explanations, it provides a viable pathway for deploying transparent AI on edge devices. This is particularly significant for practitioners seeking to implement Explainable AI (XAI) in resource-constrained healthcare environments without sacrificing diagnostic reliability.
Technical Details
- Model Architecture: qZACH-ViT is a CLS-token-free, position-free Vision Transformer backbone that generates intrinsic patch-level class evidence recursively, eliminating the need for post-hoc explanation methods.
- Optimization Method: Recursive Attribution-Stabilized Optimization (RASO) is introduced to stabilize training by norm-matching classification and attribution gradients and removing conflicting attribution components, unlike standard gradient descent.
- Quantization Implementation: The model utilizes mixed-precision ONNX INT8 graphs with 16 signed INT8 MatMulInteger projections and INT32 accumulation, achieving a 70% reduction in artifact size compared to source checkpoints.
- Benchmarking: Evaluated across seven MedMNIST datasets with rigorous controls (50 images/class, 10 seeds, 280 total runs), showing mean paired gains of 0.0313 (Adam) and 0.0368 (RASO) over FP32 baselines, with negligible metric degradation upon quantization.
Industry Insight
- Edge Deployment Viability: The demonstrated 1.41x to 2.39x CPU speedups and 70% size reduction confirm that high-fidelity, interpretable medical AI models can be effectively deployed on edge hardware, reducing reliance on cloud infrastructure.
- Stability in XAI: RASO offers a practical solution for the instability often found in gradient-based explanations, suggesting that future XAI frameworks should prioritize intrinsic optimization strategies over post-hoc attribution techniques for critical applications.
- Regulatory Compliance: The combination of intrinsic interpretability and high quantization fidelity supports regulatory requirements for transparency and reproducibility in medical diagnostics, potentially accelerating the adoption of AI-assisted tools in clinical settings.
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