Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours
By O. Iizuka, F. Kanavati, Kei Kato, Michael Rambeau, K. Arihiro, M. Tsuneki. Scientific Reports, 2020
356 citations recorded by the source corpus.
Histopathological classification of gastric and colonic epithelial tumours is one of the routine pathological diagnosis tasks for pathologists. Computational pathology techniques based on Artificial intelligence (AI) would be of high benefit in easing the ever increasing workloads on pathologists, especially in regions that have shortages in access to pathological diagnosis services. In this study, we trained convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on biopsy histopathology whole-slide images (WSIs) of stomach and colon. The models were trained to classify WSI into adenocarcinoma, adenoma, and non-neoplastic. We evaluated our models on three independent test sets each, achieving area under the curves (AUCs) up to 0.97 and 0.99 for gastric adenocarcinoma and adenoma, respectively, and 0.96 and 0.99 for colonic adenocarcinoma and adenoma respectively. The results demonstrate the generalisation ability of our models and the high promising potential of deployment in a practical histopathological diagnostic workflow system.
Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/0f129f7b3e08d82af42a69c1de1da282ba22ada0
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In Artificial Intelligence
In Oncology
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Artificial intelligence for digital and computational pathology (2023, background): “sensitivity from WSIs, allowing the identification of patients who do not require further testing 133 .”