Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Buchtitel: | MICCAI 2021 |
| Serie: | LNCS |
| Jahr: | 2021 |
| Monat: | Oktober |
| Verlag: | Springer |
| Abriss: | Being accountable for the signed reports, pathologists may be wary of high-quality deep learning outcomes if the decision-making is not understandable. Applying off-the-shelf methods with default configurations such as Local Interpretable Model-Agnostic Explanations (LIME) is not sufficient to generate stable and understandable explanations. This work improves the application of LIME to histopathology images by leveraging nuclei annotations, creating a reliable way for pathologists to audit black-box tumor classifiers. The obtained visualizations reveal the sharp, neat and high attention of the deep classifier to the neoplas-tic nuclei in the dataset, an observation in line with clinical decision making. Compared to standard LIME, our explanations show improved understandability for domain-experts, report higher stability and pass the sanity checks of consistency to data or initialization changes and sensitivity to network parameters. This represents a promising step in giving pathologists tools to obtain additional information on image classification models. The code and trained models are available on GitHub. |
| Schlagworte: | Deep Learning, explainable AI (XAI), interpretability, machine learning |
| Autoren | |
| Hinzugefügt von: | [] |
| Zugriffsrechte: | r: e: (Alle Rechte ändern) |
| Gesamtbewertung: | 0 |
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