Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology

Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology

Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases to deep learning, negatively influencing transparency, reproducibility, and classification performance. In particular, biases are well known to cause...

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Bibliographic Details
Main Authors: Daniel Sauter, Georg Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, Markus Kukuk
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
explainable AI
bias discovery
concept attribution
saliency map
computational histopathology
malignant melanoma
Online Access:https://www.mdpi.com/1424-8220/22/14/5346

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https://www.mdpi.com/1424-8220/22/14/5346

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