Multi-Task Fusion for Improving Mammography Screening Data Classification.

Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.

Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans.

We propose an automated pipeline for vessel centerline extraction in 3-D computed tomography angiography (CTA) scans with arbitrary fields of view. The principal steps of the pipeline are body part detection, candidate seed selection, segment tracking, which includes centerline extraction, and vessel tree growing. The final tree-growing step can be instantiated in either a semi- or fully automated fashion. The fully automated initialization is carried out using a vessel position regression algorithm. Both semi-and fully automated methods were evaluated on 30 CTA scans comprising neck, abdominal, and leg arteries in multiple fields of view. High detection rates and centerline accuracy values for 38 distinct vessels demonstrate the effectiveness of our approach.