Explainable convolutional neural networks for assessing head and neck cancer histopathology.

ABSTRACT:

Purpose

Although neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that addresses this problem is Explainable AI.

Methods

Here, we aimed to investigate the ability of Convolutional Neural Networks (CNNs) to classify head and neck cancer histopathology. To this end, we manually annotated 101 histopathological slides of locally advanced head and neck squamous cell carcinoma. We trained a CNN to classify tumor and non-tumor tissue, and another CNN to semantically segment four classes - tumor, non-tumor, non-specified tissue, and background. We applied Explainable AI techniques, namely Grad-CAM and HR-CAM, to both networks and explored im

SUBMITTER: Dorrich M 

PROVIDER: S-EPMC10623808 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature