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

arXiv: https://arxiv.org/abs/2201.04795 · HTML: https://mlsanity.com/paper/2201.04795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images](https://mlsanity.com/paper/1910.08978/) (2019)
- [Automatic Breast Ultrasound Image Segmentation: A Survey](https://mlsanity.com/paper/1704.01472/) (2017)
- [ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation](https://mlsanity.com/paper/2009.12894/) (2020)
- [Stan: Small tumor-aware network for breast ultrasound image segmentation](https://mlsanity.com/paper/2002.01034/) (2020)

## Built on by (most cited first, 1 of 1)

- [Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network](https://mlsanity.com/paper/2308.02101/) (2023)

## Abstract (arXiv)

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.