Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases

Autor: Iam Palatnik de Sousa, Marley Maria Bernardes Rebuzzi Vellasco, Eduardo Costa da Silva
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Sensors, Vol 19, Iss 13, p 2969 (2019)
Druh dokumentu: article
ISSN: 1424-8220
DOI: 10.3390/s19132969
Popis: An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the “locally-interpretable model-agnostic explanations” methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge.
Databáze: Directory of Open Access Journals
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