A framework for falsifiable explanations of machine learning models with an application in computational pathology.
Med Image Anal
; 82: 102594, 2022 11.
Article
em En
| MEDLINE
| ID: mdl-36058053
ABSTRACT
In recent years, deep learning has been the key driver of breakthrough developments in computational pathology and other image based approaches that support medical diagnosis and treatment. The underlying neural networks as inherent black boxes lack transparency and are often accompanied by approaches to explain their output. However, formally defining explainability has been a notorious unsolved riddle. Here, we introduce a hypothesis-based framework for falsifiable explanations of machine learning models. A falsifiable explanation is a hypothesis that connects an intermediate space induced by the model with the sample from which the data originate. We instantiate this framework in a computational pathology setting using hyperspectral infrared microscopy. The intermediate space is an activation map, which is trained with an inductive bias to localize tumor. An explanation is constituted by hypothesizing that activation corresponds to tumor and associated structures, which we validate by histological staining as an independent secondary experiment.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
/
Neoplasias
Limite:
Humans
Idioma:
En
Revista:
Med Image Anal
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2022
Tipo de documento:
Article