computer vision

Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network

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 …

[Paper Presentation] Deep Anomaly Detection with Outlier Exposure

Presented an interesting paper from ICLR 2019 that performs out-of-distribution sample detection using a simple framework.

[Paper Presentation] Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations

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

[Paper Presentation] Interactive Live-Wire Boundary Extraction

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

Dealing with Missing Modalities in Medical Images What's Missing?

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

A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks

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 …

Select, Attend, and Transfer: Light, Learnable Skip Connections

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 Tutorial on Reinforcement Learning

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.

[Paper Presentation] Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

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

Predicting Survival Time for Patients Diagnosed with Gliomas Using Compressed Representation of Brain MRI Scans

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