Multi-scale local explanation approach for image analysis using model-agnostic Explainable Artificial Intelligence (XAI)
Bibliographic record
Abstract
The recent success of deep neural networks has generated remarkable growth in Artificial Intelligence (AI) research and has received much interest over the past few years. One of the main challenges for the broad adoption of deep learning-based models such as Convolutional Neural Networks (CNN) is the lack of understanding of their decisions. To address this issue, Explainable Artificial Intelligence (XAI) has been proposed to shift toward more transparent AI, resulting in the development of techniques to explain decisions by AI models. This paper aims to explore and develop a multi-scale scheme of LIME (Local Interpretable Model-Agnostic Explanations) applied to image classification to explain decisions made by CNN models through heatmaps of coarse to finer scales. More precisely, when LIME highlights large superpixels from a coarse scale, there may be smaller regions in the corresponding superpixel that influenced the model’s prediction at some finer scale. In the proposed multi-scale scheme, two weighting approaches, one based on Gaussian distribution and another parameter-free framework will be introduced to produce visual explanations observed from different scales. Promising results for multi-scale classification heatmaps of histopathology images are presented. More specifically, we investigated the proposed multi-scale approach on Camelyon16 dataset. The results show that the explanations are faithful to the underlying model, and the visualizations are reasonably interpretable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".