The pathognomonic area for each class was appropriately highlighted in digital images by saliency map, particularly focusing epithelial lesions. Deep learning models achieved excellent performances for discriminating adenocarcinoma from non-adenocarcinoma lesions with an AUC of 0.995 or 0.998. The per-class area under the receiver operating characteristic curve (AUC) was highest for adenocarcinoma (1.000 95% CI 0.999–1.000) by DenseNet-161 and TSA (1.000 95% CI 1.000–1.000) by EfficientNet-B7. For six-class classification, the mean diagnostic accuracy was 97.3% (95% confidence interval, 96.0–98.6%) by DenseNet-161 and 95.9% (95% CI 94.1–97.7%) by EfficientNet-B7. A total of 1865 images were included from 703 patients, of which 10% were used as a test dataset. Digital photographs were taken of each pathological slide to fine-tune two pre-trained convolutional neural networks, and the model performances were evaluated.
Histopathological slides of colonoscopic biopsy or resection specimens were collected and grouped into six classes by disease category: adenocarcinoma, tubular adenoma (TA), traditional serrated adenoma (TSA), sessile serrated adenoma (SSA), hyperplastic polyp (HP), and non-specific lesions. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtained from colonoscopy-related specimen. Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis.
0 kommentar(er)
