STL‐DeepBDC: A Robust Few‐Shot Learning Framework for Multiclass Ovarian Tumor Classification in Ultrasound Outperforms Conventional Transfer Learning

Abstract

Accurate preoperative pathological classification of malignant and borderline ovarian tumors (OMTs) can support surgical planning, fertility preservation, and prognosis, but ultrasound‐based subtype assessment remains difficult because imaging phenotypes are heterogeneous, class imbalance is common, and interpretation varies among operators. This study developed and evaluated an exploratory deep learning framework for multiclass ovarian tumor classification under small‐sample conditions.