Self-supervised Domain Adaptation with Significance-Oriented Masking for Pelvic Organ Prolapse detection

Abstract

Pelvic Organ Prolapse (POP) is a common disease in middle-aged and elderly women. The detection of POP is a challenging task, and using deep learning for detection has its practical significance. However, medical image detection tasks always face many problems, i.e., small sample size, data imbalance, and unobvious pathological characteristics. In this paper, we propose a new training framework, called self-supervised Domain Adaptation with Significance-Oriented Masking (DASOM), to address these problems and improve the performance of POP intelligent detection. DASOM includes a new pre-training process based on the masked image modeling task, and redesigns the masking strategy, bringing the local induction capability required for detection to the model. Meanwhile, we also adopt the data process method fitting the pelvic floor ultrasonic dataset to effectively solve the problem of data shortage and imbalance. Extensive experimental results and analysis confirm that the proposed method significantly improves the performance and reliability of POP detection.