计算机科学
判别式
人工智能
特征(语言学)
水准点(测量)
特征学习
病变
上下文图像分类
代表(政治)
机器学习
模式识别(心理学)
医学影像学
自然语言处理
图像(数学)
医学
病理
语言学
哲学
大地测量学
政治
政治学
法学
地理
作者
Tang Yong,Gang Yangt,Jianchun Zhao,Dayong Ding,Jun Wu
标识
DOI:10.1109/icme55011.2023.00170
摘要
Recently, contrastive learning has received significant attention in various classification tasks of natural images. However, current contrastive learning frameworks display unsatisfactory performance on medical images due to the inability of obtaining fine-grained visual features. In this paper, we propose a Lesion-Aware Contrastive Learning (LACL) framework to learn more discriminative and comprehensive representations and reinforce the attention of diagnosis regions on medical images. LACL framework includes two phases of training procedures: the comprehensive feature-extracting phase and the contrastive learning enhancement phase. In the first phase, LACL fully captures meaningful deep features related to the training targets to form comprehensive visual representations, by training a novel lesion-aware module we proposed. In the second phase, we introduce the previous representation information into contrastive learning to guide the LACL framework in learning disease- related features. This approach provides more effective guidance than the traditional contrastive learning method of directly comparing features. Extensive experiments on several benchmark datasets demonstrate that our LACL framework significantly improves the performance of medical image classification and highlights the lesion areas for disease diagnosis.
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