Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.
Source: PubMed, NCBI / U.S. National Library of Medicine
Colorectal cancer (CRC) is a leading cause of mortality worldwide, and early examination via colonoscopy is increasingly used to prevent CRC mortality. Recently, studies have attempted utilizing artificial intelligence for the classification of CRC. However, datasets were limited in these studies, and the limited number of findings resulting from these studies are not specific to cancerous or noncancerous findings. We present a full pipeline of ensemble deep learning approach to classify five diagnostic categories. Through this pipeline, whole slide images (WSIs) exhibiting low quality can be filtered out before being processed by the computer-aided diagnosis system. A dataset of 18,922 CRC WSIs collected and labeled as non-tumor, hyperplastic polyp, adenoma, adenocarcinoma, and neuroendocrine tumor (NET, carcinoid). We developed two models: clustering-constrained attention multiple instance learning model was used to classify adenocarcinomas, adenomas, NET, and non-dysplastic lesion classes. EfficientNet then distinguished between non-tumor and hyperplastic polyp for WSIs classified as non-dysplastic lesion class in the first step. The proposed ensemble pipeline, which resembles the process of analysis from the pathologists, showed better performance even with multiple classes CRC. The micro-, macro-, and weighted-F1-scores were 86.57%, 83.83%, and 86.86%, respectively. Notably, the NET classification tasks scored an F1-score of 87.18%. The proposed ensemble model addresses
Abstract
Colorectal cancer (CRC) is a leading cause of mortality worldwide, and early examination via colonoscopy is increasingly used to prevent CRC mortality. Recently, studies have attempted utilizing artificial intelligence for the classification of CRC. However, datasets were limited in these studies, and the limited number of findings resulting from these studies are not specific to cancerous or noncancerous findings. We present a full pipeline of ensemble deep learning approach to classify five diagnostic categories. Through this pipeline, whole slide images (WSIs) exhibiting low quality can be filtered out before being processed by the computer-aided diagnosis system. A dataset of 18,922 CRC WSIs collected and labeled as non-tumor, hyperplastic polyp, adenoma, adenocarcinoma, and neuroendocrine tumor (NET, carcinoid). We developed two models: clustering-constrained attention multiple instance learning model was used to classify adenocarcinomas, adenomas, NET, and non-dysplastic lesion classes. EfficientNet then distinguished between non-tumor and hyperplastic polyp for WSIs classified as non-dysplastic lesion class in the first step. The proposed ensemble pipeline, which resembles the process of analysis from the pathologists, showed better performance even with multiple classes CRC. The micro-, macro-, and weighted-F1-scores were 86.57%, 83.83%, and 86.86%, respectively. Notably, the NET classification tasks scored an F1-score of 87.18%. The proposed ensemble model addresses common challenges in automated CRC diagnosis, offering reliable solutions with safeguards for exceptional cases. It enhances clinical practice by accurately classifying multiple CRC types. In conclusion, this method is effective where scanned colorectal WSIs can be of low quality for real-world diagnosis.