Ultrasound Image Multi-Instance Learning With Global-Neighbor Awareness and Adaptive Bilinear Pooling for HT Recognition

Ultrasound imaging can reveal the typical changes in thyroid tissue caused by Hashimoto's Thyroiditis (HT), which plays a crucial role in HT diagnosis. Clinicians should perform ultrasound imaging from multiple anatomical planes to obtain a series of thyroid images, thereby collecting comprehensive information about HT lesions from different perspectives. However, ultrasound images lacking typical HT characteristics or containing extensive non-HT regions may hinder the identification of localized lesions, thereby increasing the complexity of diagnosis. In addition, the ability to capture synergistic interactions among multiple lesions in critical ultrasound images is crucial for improving the diagnostic accuracy of HT. To address these challenges, a novel weakly supervised multi-instance learning model, HTMIL, is proposed for HT diagnosis, requiring only patient-level data annotation. HTMIL consists of a Global-Neighbor extraction Layer (GNL) and a Cross-Aggregation Layer (CAL). Specifically, a Global-Neighbor Awareness (GNA) module is proposed in GNL to allow HTMIL to focus on key localized lesions and ignore noise from unrelated regions in HT ultrasound images, thus improving the effectiveness of extracting focused features from localized lesions. The Adaptive Bilinear Pooling (ABP) module is introduced in CAL to capture the interaction features between key localized lesions and other characteristics of HT in critical ultrasound images, thus achieving synergistic diagnosis and further increasing diagnostic accuracy. HTMIL achieves state-of-the-art (SOTA) performance with 87.67% accuracy and 91.73% AUC on a multicenter HT dataset, and its robustness is further validated on a public dataset.