Dancing with Discrepancies: Commonality Specificity Attention GAN for Weakly Supervised Medical Lesion Segmentation
Keywords: medical image segmentation, weakly supervised segmentation
Abstract: Increasing weakly supervised semantic segmentation methods concentrate on the target segmentation by leveraging solely image-level labels. However, few works notice that a significant gap exists in addressing medical characteristics, which demands massive attention. In this paper, we note: (i) Lesion regions typically exhibit a sharp probability distribution pattern while healthy tissues adhere to an underlying homogeneous distribution, which deviates from typical natural images; (ii) Boundaries of lesion foregrounds and structural backgrounds are blurred; (iii) Similar structures frequently appear within specific organs or tissues, which poses a challenge to concentrating models’ attention on regions of interest instead of the entire image. Thus we propose a Commonality-specificity attention GAN (CoinGAN) to overcome the above challenges, which leverages distribution discrepancies to mine the knowledge underlying images. Specifically, we propose a new form of convolution, contrastive convolution, to utilize the fine-grained perceptual discrepancies of activation sub-maps to enhance the intra-image distribution, making lesion foregrounds (specificity) and structural backgrounds (commonality) boundary-aware. Then a commonality-specificity attention mechanism and the GAN-based loss function are devised to jointly suppress similarity regions between different labels of images and accentuate discrepancy regions between different labels of images. This isolates lesion areas from the structural background. Extensive experiments are conducted on three public benchmarks. Our CoinGAN achieves state-of-the-art performance with the DSC of 71.69%, 84.73%, and 78.32% on QaTa-COV19, ISIC2018, and MoNuSeg datasets, making a significant contribution to the detection of pneumonia, skin disease, and cancer. Furthermore, the visualized results also corroborate the effectiveness of CoinGAN in segmenting medical objects.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 544
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