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An effective colorectal polyp classification for histopathological images based on supervised contrastive learning
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2024-03-08 , DOI: 10.1016/j.compbiomed.2024.108267 Sena Busra Yengec-Tasdemir 1 , Zafer Aydin 2 , Ebru Akay 3 , Serkan Dogan 4 , Bulent Yilmaz 5
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2024-03-08 , DOI: 10.1016/j.compbiomed.2024.108267 Sena Busra Yengec-Tasdemir 1 , Zafer Aydin 2 , Ebru Akay 3 , Serkan Dogan 4 , Bulent Yilmaz 5
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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.
中文翻译：
基于监督对比学习的组织病理学图像的有效结直肠息肉分类
早期发现结肠腺瘤性息肉对于降低结肠癌风险至关重要。在这种情况下，准确区分腺瘤性息肉亚型，尤其是管状和管状绒毛状，与增生性息肉亚型至关重要。本研究介绍了针对该任务优化的尖端计算机辅助诊断系统。我们的系统采用先进的监督对比学习来确保结肠组织病理学图像的精确分类。值得注意的是，我们集成了 Big Transfer 模型，该模型因其对医学成像中视觉任务的出色适应性而受到关注。我们的新颖方法可以区分类内和类外图像，从而提高其对息肉亚型的区分能力。我们使用两个数据集验证了我们的系统：一个是专门策划的数据集，另一个是可公开访问的 UniToPatho 数据集。结果表明，我们的模型明显超越了传统的深度卷积神经网络，在自定义数据集和 UniToPatho 数据集上的分类准确率分别为 87.1% 和 70.3%。这些结果强调了我们的模型在息肉分类工作中的变革潜力。
更新日期：2024-03-08
中文翻译：
基于监督对比学习的组织病理学图像的有效结直肠息肉分类
早期发现结肠腺瘤性息肉对于降低结肠癌风险至关重要。在这种情况下，准确区分腺瘤性息肉亚型，尤其是管状和管状绒毛状，与增生性息肉亚型至关重要。本研究介绍了针对该任务优化的尖端计算机辅助诊断系统。我们的系统采用先进的监督对比学习来确保结肠组织病理学图像的精确分类。值得注意的是，我们集成了 Big Transfer 模型，该模型因其对医学成像中视觉任务的出色适应性而受到关注。我们的新颖方法可以区分类内和类外图像，从而提高其对息肉亚型的区分能力。我们使用两个数据集验证了我们的系统：一个是专门策划的数据集，另一个是可公开访问的 UniToPatho 数据集。结果表明，我们的模型明显超越了传统的深度卷积神经网络，在自定义数据集和 UniToPatho 数据集上的分类准确率分别为 87.1% 和 70.3%。这些结果强调了我们的模型在息肉分类工作中的变革潜力。
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