PSScreen: Partially Supervised Multiple Retinal Disease Screening

TL;DR

PSScreen is a novel model that leverages partially labeled datasets and combines deterministic and probabilistic features with textual guidance and self-distillation to improve retinal disease screening across diverse datasets.

Contribution

The paper introduces PSScreen, a new partially supervised model that uses dual feature streams, textual guidance, and self-distillation to enhance multi-disease retinal screening performance.

Findings

Significantly improves detection accuracy for six retinal diseases.

Achieves state-of-the-art results on both in-domain and out-of-domain datasets.

Effectively handles domain shifts and partial labels in retinal disease datasets.

Abstract

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.