A Brain-Inspired Dual-Stream Neural Network for Tumor Classification in Ultrasound Images

Early and accurate tumor classification in ultrasound images plays a pivotal role in improving cancer diagnosis and patient outcomes. Existing computer-aided diagnostic (CAD) algorithms often rely on cropping-based single feedforward pathways, which can result in the loss of crucial contextual information around the tumor. The surrounding ultrasound data, including relative intensity, plays a significant role in tumor diagnosis, and incorrect cropping or positioning may lead to unreliable results. To overcome these limitations, we propose a novel Brain-inspired Dual-stream Network (BidsNet), aiming to emulate the functional mechanisms of the dorsal and ventral streams in human visual processing. BidsNet processes the entire ultrasound image as input, preventing errors or loss of contextual details from cropping. The dorsal stream in BidsNet specializes in extracting spatial features, such as shape and texture, while the ventral stream focuses on object recognition and classification. A cross-stream communication mechanism is introduced to facilitate dynamic information sharing between the streams: spatial attention generated in the dorsal stream informs the ventral stream to improve feature localization, while channel attention derived from the ventral stream refines spatial feature representation in the dorsal stream. This collaborative interplay boosts both the interpretability and performance of the network. Extensive experiments on multiple ultrasound datasets demonstrate that BidsNet delivers superior accuracy and interpretability, validating the effectiveness of its dual-stream design and cross-stream communication mechanism.