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Lesion region segmentation via weakly supervised learning

Yi, Ran, Weng, Yang, Yu, Mingjing, Lai, Yu-Kun ORCID: and Liu, Ying-Jin 2022. Lesion region segmentation via weakly supervised learning. Quantitative Biology 10 (3) , pp. 239-252. 10.15302/J-QB-021-0272

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Background: Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance. However, crop lesion regions tend to be scattered and of varying sizes, this along with substantial intra-class variation and small inter-class variation makes segmentation difficult. Methods: We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region segmentation. First, a two-branch network is designed for joint disease classification and seed region generation. The generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder network. Different from previous works that use an encoder in the segmentation network, the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions, which is the major challenge in image-based diagnosis of field diseases. We further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network, making use of the extracted seed regions. Results: Experimental results show that our system achieves better lesion region segmentation results than state of the arts. In addition to crop images, our method is also applicable to general scattered object segmentation. We demonstrate this by extending our framework to work on the PASCAL VOC dataset, which achieves comparable performance with the state-of-the-art DSRG (deep seeded region growing) method. Conclusion: Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task, but also shows its capability to perform well on more general tasks.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
ISSN: 2095-4689
Date of First Compliant Deposit: 2 September 2021
Date of Acceptance: 6 August 2021
Last Modified: 26 May 2023 01:31

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