You are cordially invited to attend our next Computer Graphics and Visualization (CGV) Seminar/ Colloquium, which will be held on:
Friday, May 25th, 2018, 15:45-17:45h, at EWI-Lecture Hall Pi.
The programme features one MSc graduation project midterm presentations and a presentation of a research project by one of our PhD students.
Presenter 1: Yunchao Yin
Title: Annotation of cerebral angiography,
Abstract: Cerebral angiography is a medical imaging technique that used to visualize the vessels around the brain and provide quantitative measures for pathological changes such as arteriovenous malformations and aneurysms. However, it’s difficult for patients and young clinical stuff to tell the name of each vessel in the angiography. This project plans to create an automatic cerebral vessel name annotation tool based on deep learning. Semantic segmentation and topological skeleton extraction are both possible to realize automatic vessel names annotation, but the ground truth for semantic segmentation is relatively hard to get and pixel-wise perfect is not required for this task, so skeleton extraction is chosen. The project could be divided into two part: 1) angiography acquirement angle classification; 2) Vessel bifurcation detection. Different cerebral vascular model is used for angiography annotation of different projection angles, and the second part of the project predicts vessel names.
Presenter 2: Chaoran Fan
Title: Fast and accurate CNN-based brushing in scatterplots
Abstract: Brushing plays a central role in most modern visual analytics solutions and effective and efficient techniques for data selection are key to establishing a successful human-computer dialogue. With this paper, we address the need for brushing techniques that are both fast, enabling a fluid interaction in visual data exploration and analysis, and also accurate, i.e., enabling the user to effectively select specific data subsets, even when their geometric delimination is non-trivial. We present a new solution for a near-perfect sketch-based brushing technique, where we exploit a convolutional neural network (CNN) for estimating the intended data selection from a fast and simple click-and-drag interaction and from the data distribution in the visualization. Our key contributions include a drastically reduced error rate—now below 3%, i.e., less than half of the so far best accuracy— and an extension to a larger variety of selected data subsets, going beyond previous limitations due to linear estimation models.