medical image analysis

Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in the form of MR pulse sequences in a single scan provides …

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the …

This talk covers a classical method for shape registration using approaches from information theory and deformations.

Paper presentation for the Live-Wire segmentation method, that was famously used in Adobe Photoshop.

A talk regarding the current state-of-art in handling missing inputs for segmentation or classification problems.

Inspired by the success of Convolutional Neural Networks (CNN), we develop a novel Computer Aided Detection (CADe) system using CNN for Glioblastoma Multiforme (GBM) detection and segmentation from multi channel MRI data. A two-stage approach first …

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients. They equip encoder-decoder-like networks …

A mathematical, in-depth tutorial about reinforcement learning presented to the lab members. This was to facilitate members to take up RL methods and apply them to their respective problem areas, as well as for myself to understand RL in an in-depth way.

This talk covers the first known application of reinforcement learning agents for landmark detection in CT Scans in medical imaging.

Convolutional neural networks based autoencoders for generating low-dimensional representations of brain MRI scans.