Weakly Supervised Learning for Multi-modal Breast Lesion Classification in Ultrasound and Mammogram Images

Type: MA thesis

Status: finished

Date: April 15, 2020 - October 15, 2020

Supervisors: Sulaiman Vesal, Dalia Rodriguez Salas, Andreas Maier, Tino Haderlein

Breast cancer has become one of the most common and leading types of cancer in women, which has taken death rate of 11.6 percent of the total cancer deaths worldwide. The mortality rate is increasing in recent years. It must be emphasized that early detection of a breast tumor can help to increase early treatment options that control the mortality rate among women. There are different diagnostic imaging modalities, which help doctors diagnose whether the patient is under the risk of possessing cancerous tumor.

Imaging modalities like ultrasound and mammogram are both used for screening of breast lesions. Mammogram, on the one hand, uses low radiation dose and takes an image of the breast as a 2-D image. Ultrasound, on the other hand, uses high frequency waves capturing an image of the breast as a 3-D image. Both modalities capture different useful information with their acquisition methods. Patients usually undergo diagnosis with mammography for initial lesion detection. But due to its low sensitivity, there are chances to miss detection of small tumors in heavy and dense breasts. Those patients that are highly suspected to the abnormalities are further diagnosed with ultrasound. Ultrasound images give more detailed information about the surrounding area of concern and hence also help radiologists investigate the vulnerability of the lesion.

The main aim of this thesis is to investigate the performance of deep learning models for classification of breast lesion using a dataset of ultrasound and mammogram images individually. Further, based on the evaluation of the performance results of these models, we would build a single deep learning model, which combine the information from both ultrasound and mammogram imaging modalities. An analysis of the performance of the fused and the individual models will also be performed.
The dataset which will be used to train the models consists of volumetric ultrasound images and 2-D mammogram images and is provided by University Clinics Erlangen. Weakly supervised approaches will be used with the classification labels defined at image level without further localisation. There are 468 patient files consisting of ultrasound and mammogram images of healthy and non-healthy patients. The latter can have either benign or malignant lesions.