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Biol."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Image\u2010based 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\u2010class variation and small inter\u2010class variation makes segmentation difficult.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a novel end\u2010to\u2010end system that only requires weak supervision of image\u2010level labels for lesion region segmentation. First, a two\u2010branch 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\u2010decoder network. Different from previous works that use an encoder in the segmentation network, the encoder\u2010decoder network is critical for our system to successfully segment images with small and scattered regions, which is the major challenge in image\u2010based diagnosis of field diseases. We further propose a novel weakly supervised training strategy for the encoder\u2010decoder semantic segmentation network, making use of the extracted seed regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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\u2010of\u2010the\u2010art DSRG (deep seeded region growing) method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our method not only outperforms state\u2010of\u2010the\u2010art semantic segmentation methods by a large margin for the lesion segmentation task, but also shows its capability to perform well on more general tasks.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-021-0272","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:58:45Z","timestamp":1628470725000},"page":"239-252","source":"Crossref","is-referenced-by-count":4,"title":["Lesion region segmentation via weakly supervised learning"],"prefix":"10.1002","volume":"10","author":[{"given":"Ran","family":"Yi","sequence":"first","affiliation":[{"name":"<!--1--> Department of Computer Science and Technology Tsinghua University Beijing 100084 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zeng","sequence":"additional","affiliation":[{"name":"<!--1--> Department of Computer Science and Technology Tsinghua University Beijing 100084 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Weng","sequence":"additional","affiliation":[{"name":"<!--1--> Department of Computer Science and Technology Tsinghua University Beijing 100084 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjing","family":"Yu","sequence":"additional","affiliation":[{"name":"<!--2--> College of Intelligence and Computing Tianjin University Tianjin 300350 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu\u2010Kun","family":"Lai","sequence":"additional","affiliation":[{"name":"<!--3--> School of Computer Science and Informatics Cardiff University Cardiff CF10 3AT United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong\u2010Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"<!--1--> Department of Computer Science and Technology Tsinghua University Beijing 100084 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,9]]},"reference":[{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.phyto.43.113004.133839"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.baae.2009.12.001"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"Aravind K. 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