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Weakly Supervised PET Tumor Detection Using Class Response
[article]
2020
arXiv
pre-print
One of the most challenges in medical imaging is the lack of data and annotated data. It is proven that classical segmentation methods such as U-NET are useful but still limited due to the lack of annotated data. Using a weakly supervised learning is a promising way to address this problem, however, it is challenging to train one model to detect and locate efficiently different type of lesions due to the huge variation in images. In this paper, we present a novel approach to locate different
arXiv:2003.08337v2
fatcat:ipm6x22nz5fabcnmnp4jay32ye