Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs
This addresses the challenge of expensive and time-consuming data labeling in medical imaging, particularly for novel target domains, though it appears incremental as it builds on existing cross-domain adaptation methods.
The paper tackles the problem of unsupervised cross-domain adaptation for segmenting 7 layers in OCT images, enabling deep learning in medical domains where labeled target data is unavailable, achieving results that generalize to novel target domains without requiring expensive labeling.
Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in the target domain. Our proposed method helps generalize of deep learning to many areas in the medical field where labeled training data are expensive and time consuming to acquire or where target domains are too novel to have had labelling.