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- W4200468497 abstract "Breast ultrasound (BUS) tumor segmentation can help with the early detection of breast cancer; however, the lack of human-labeled training data is a big problem in this area. To deal with this, we propose a novel self-supervised learning approach (CR-SSL), where several related pretext tasks like unsupervised segmentation and edge detection are firstly learned, followed by the target tumor segmentation based fine-tuning. Learning such related pretext tasks ensures better representation learning. Experimental study shows that CR-SSL can improve the mean Dice and Jaccard scores by more than 4–5%, with ≥0.6 scores while having access to only 20–50 human-labeled training samples. The best Dice scores of 0.7303 and 0.8207, and Jaccard scores of 0.7082 and 0.8015 are obtained by CR-SSL on the test datasets. Compared with the No-SSL baseline, CR-SSL can achieve (10–20)% improvements in segmentation quality while working in small-sized training dataset scenarios, suggesting its high potential practical utility." @default.
- W4200468497 created "2021-12-31" @default.
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- W4200468497 date "2021-12-27" @default.
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- W4200468497 title "<scp>CR‐SSL</scp> : A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation" @default.
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- W4200468497 doi "https://doi.org/10.1002/ima.22693" @default.
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