EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer

TL;DR

This paper introduces EMT-NET, a lightweight multitask deep learning model for breast cancer detection that combines classification and segmentation, achieving high accuracy and fast inference suitable for real-world use.

Contribution

It presents a novel efficient multitask architecture with a stable loss function, improving breast tumor detection performance while reducing computational complexity.

Findings

Accuracy of 88.6% in tumor classification

Inference time of 0.35 seconds per image

High sensitivity of 94.1% in detection

Abstract

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that easily controls the balance between the sensitivity and specificity of cancer detection. The proposed approach is evaluated using a breast ultrasound dataset with 1,511 images. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively. We validate the model using a virtual mobile device, and the average inference time is 0.35 seconds per image.