Web: http://arxiv.org/abs/2206.08791

June 20, 2022, 1:13 a.m. | Yilong Li, Yaqi Wang, Huiyu Zhou, Huaqiong Wang, Gangyong Jia, Qianni Zhang

cs.CV updates on arXiv.org arxiv.org

In this paper, we introduce an unsupervised cancer segmentation framework for
histology images. The framework involves an effective contrastive learning
scheme for extracting distinctive visual representations for segmentation. The
encoder is a Deep U-Net (DU-Net) structure that contains an extra fully
convolution layer compared to the normal U-Net. A contrastive learning scheme
is developed to solve the problem of lacking training sets with high-quality
annotations on tumour boundaries. A specific set of data augmentation
techniques are employed to improve the …

arxiv cancer cv images learning segmentation unsupervised

More from arxiv.org / cs.CV updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY