PSScreen: Partially Supervised Multiple Retinal Disease Screening
This addresses the problem of limited fully annotated medical datasets for ophthalmologists by improving multi-disease screening with partial supervision, though it appears incremental as it builds on existing domain adaptation and pseudo-labeling techniques.
The paper tackles the challenge of training retinal disease screening models using partially labeled datasets from multiple medical sites with domain shifts, proposing PSScreen which uses dual-stream feature learning with uncertainty injection and feature distillation. The method achieves state-of-the-art results on six retinal diseases and normal state detection across in-domain and out-of-domain datasets.
Leveraging multiple partially labeled datasets to train a model for multiple retinal disease screening reduces the reliance on fully annotated datasets, but remains challenging due to significant domain shifts across training datasets from various medical sites, and the label absent issue for partial classes. To solve these challenges, we propose PSScreen, a novel Partially Supervised multiple retinal disease Screening model. Our PSScreen consists of two streams and one learns deterministic features and the other learns probabilistic features via uncertainty injection. Then, we leverage the textual guidance to decouple two types of features into disease-wise features and align them via feature distillation to boost the domain generalization ability. Meanwhile, we employ pseudo label consistency between two streams to address the label absent issue and introduce a self-distillation to transfer task-relevant semantics about known classes from the deterministic to the probabilistic stream to further enhance the detection performances. Experiments show that our PSScreen significantly enhances the detection performances on six retinal diseases and the normal state averagely and achieves state-of-the-art results on both in-domain and out-of-domain datasets. Codes are available at https://github.com/boyiZheng99/PSScreen.