An effective colorectal polyp classification for histopathological images based on supervised contrastive learning

被引：2
作者：
Yengec-Tasdemir S.B. [1 ]
Aydin Z. [2 ,3 ]
Akay E. [4 ]
Dogan S. [5 ]
Yilmaz B. [2 ,6 ]
机构：
[1] School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast
[2] Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri
[3] Department of Computer Engineering, Abdullah Gul University, Kayseri
[4] Pathology Clinic, Kayseri City Hospital, Kayseri
[5] Gastroenterology Clinic, Kayseri City Hospital, Kayseri
[6] Department of Electrical Engineering, Gulf University for Science and Technology, Mishref
来源：
Computers in Biology and Medicine | 2024年 / 172卷
关键词：
Big transfer; Colonic polyp classification; Computer-aided diagnosis; Histopathology image classification; Supervised contrastive learning; Transfer learning;
D O I：
10.1016/j.compbiomed.2024.108267
中图分类号：
学科分类号：
摘要：
Early detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 The Author(s)
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