PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
Unsupervised pathology detection trains models on non-pathological data to
flag deviations as pathologies, offering strong generalizability for
identifying novel diseases and avoiding costly annotations. However, building
reliable normality models requires vast healthy datasets, as hospitals' data is
inherently biased toward symptomatic populations, while privacy regulations
hinder the assembly of representative healthy cohorts. To address this
limitation, we propose PathoSCOPE, a few-shot unsupervised pathology detection
framework that requires only a small set of non-pathological samples (minimum 2
shots), significantly improving data efficiency. We introduce Global-Local
Contrastive Loss (GLCL), comprised of a Local Contrastive Loss to reduce the
variability of non-pathological embeddings and a Global Contrastive Loss to
enhance the discrimination of pathological regions. We also propose a
Pathology-informed Embedding Generation (PiEG) module that synthesizes
pathological embeddings guided by the global loss, better exploiting the
limited non-pathological samples. Evaluated on the BraTS2020 and ChestXray8
datasets, PathoSCOPE achieves state-of-the-art performance among unsupervised
methods while maintaining computational efficiency (2.48 GFLOPs, 166 FPS).
