UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision

Ufaq Khan, Umair Nawaz, Abdulmotaleb El-Saddik

Abstract: In breast ultrasound imaging, the scarcity of detailed annotations poses a major barrier to developing robust object detection models. This challenge is compounded by the high intra-class variability, where different slices of a 3D object can appear drastically different in 2D images, and low inter-class variance, where pathological features are often small and subtle compared to the rest of the image. These factors make it difficult to train models that require precise bounding box annotations or extensive labeled datasets. Addressing these issues, this study introduces a novel weakly supervised object detection (WSOD) model that capitalizes on image-level labels, which are more readily available and require significantly less effort from medical professionals. Our approach integrates Multi-Instance Learning (MIL) and Self-Supervised Learning (SSL) within a Deformable DETR framework, aiming to focus the model’s attention on relevant regions without detailed annotations. Tested on the two publicly available datasets, our model demonstrates significant improvements in mean average precision (mAP) and recall, surpassing existing state-of-the-art methods. Ablation studies confirm the essential importance of MIL and SSL in enhancing detection accuracy, validating our model as a potent solution for overcoming data scarcity in medical imaging.