Multi-tasking Breast Density Classification

Project Leaders

Xinye Wang

Tianyu Xie

Partner Organisations

江苏集萃苏科思科技有限公司

Mobile ultrasound devices play a crucial role in emergency and family doctor services, providing valuable diagnostic capabilities in a portable form factor. However, when compared to traditional medical ultrasound equipment, there is still scope for improvement in terms of image quality.

We propose the Hyper-CycleGAN model as a solution to enhance the image quality of mobile ultrasound devices. This model aims to establish a structure-preserving mapping between low-quality ultrasound images and their corresponding high-quality counterparts. Additionally, we plan to incorporate an attention mechanism into the model to enable it to better focus on important areas within the image, thus further enhancing the diagnostic relevance of the generated high-quality images. By leveraging advanced techniques and algorithms, our approach aims to bridge the gap in image quality between mobile ultrasound devices and traditional medical ultrasound equipment, thereby improving the accuracy and effectiveness of emergency and family doctor services.

Multi-tasking Breast Density Classification

The study of breast dense tissue holds immense significance in the evaluation of breast health, early detection of breast diseases, and risk assessment of breast cancer. Breast density refers to the presence of a higher proportion of glands and connective tissue in the breast, relative to fat tissue. Research has indicated that women with denser breast tissue have a higher risk of developing breast cancer compared to those with a higher proportion of fat tissue. Therefore, investigating dense breast tissue plays a crucial role in enabling doctors to more accurately assess breast health. Our research primarily focuses on employing various techniques to segment Magnetic Resonance Imaging (MRI) images of breasts and identify dense tissues, in order to calculate breast density. This enables initial screening and assessment of breast health, as well as evaluating the risk of breast cancer.

Project Example

The first image is the MRI original image of the breast, the second image is the segmentation of dense tissue within the breast, and the third image is the segmentation of the entire breast

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