Tomasz Bartczak
Tomasz is a Machine Learning Engineer at Cydar Medical. He works on variety of medical imaging analysis - segmentation, classification models, combining all that to shape the future of the software supporting endovascular aortic repair. He contributes to all the stages of the machine learning project - from the problem statement definition, through literature review and experimentation, up to deployment & monitoring. In the previous years he was developing large-scale ranking models in an e-commerce platform. Before focusing on machine learning he worked in general software engineering.
Sessions
Recent progress in deep learning for medical imaging has led to impressive results. Among them is a fully automatic human organ segmentation from Computed Tomography (CT) scans. Organ segmentation can be the end goal in itself, e.g. when it is directly viewed by clinical teams. It can also serve as an input to diagnostic aid tools. Moreover, specific knowledge can be extracted out of segmentations to build databases. These databases can then be used for reasoning about the anatomy or planning treatment.
In this talk, we will describe a multi-stage pipeline for processing CT scans for abdominal aortic aneurysm (AAA) treatment planning. We will share our experience in sub-organ multilabel segmentation. We will discuss the challenges with common loss functions, and with metrics not being well aligned with clinical significance. We will show how enhanced segmentation can be used to represent patient anatomy in an accessible way for end-users who plan treatment for new patients.