Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis
This addresses the need for faster and more efficient diagnosis tools in medical imaging, though it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of separate models for tumor localization and cancer classification in breast ultrasound by proposing an end-to-end multi-task architecture, achieving 79.8% and 86.4% segmentation performance with improved time efficiency.
Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.