Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. The methodology leverages the VGG19 convolutional neural network architecture and utilizes three publicly available COVID-19 chest radiograph datasets, consisting of over 1000 labeled images, which were preprocessed through resizing, normalization, and augmentation to ensure robustness and generalizability. The experiments focused on key performance metrics, including interpretability, structural similarity (SSIM), diagnostic precision, mean squared error (MSE), and computational efficiency. The results demonstrate that PLI significantly outperforms Grad-CAM in all measured dimensions. PLI produced detailed pixel-level heatmaps with higher SSIM scores, reduced MSE, and faster inference times, showcasing its ability to provide granular insights into localized diagnostic features while maintaining computational efficiency. In contrast, Grad-CAM’s explanations often lack the granularity required for clinical reliability. By integrating fuzzy logic to enhance visual and numerical explanations, PLI can deliver interpretable outputs that align with clinical expectations, enabling practitioners to make informed decisions with higher confidence. This work establishes PLI as a robust tool for bridging gaps in AI model transparency and clinical usability. By addressing the challenges of interpretability and accuracy simultaneously, PLI contributes to advancing the integration of AI in healthcare and sets a foundation for broader applications in other high-stake domains.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it