TY  - CONF
T1  - Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability
A1  - Graziani, Mara
A1  - Palatnik de Sousa, Iam
A1  - Vellasco BR, Marley M
A1  - Costa da Silva, Eduardo
A1  - Müller, Henning
A1  - Andrearczyk, Vincent
TI  - MICCAI 2021
T3  - LNCS
Y1  - 2021
PB  - Springer
KW  - Deep Learning
KW  - explainable AI (XAI)
KW  - interpretability
KW  - machine learning
N2  - 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.
ER  -