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I am a PhD student at the Insitute of Cancer Research and Imperial College London researching Deep Learning in Cancer Biology under the supervision of Chris Bakal and Chris Dunsby. My PhD is part of the CRUK Accelerator award. I have an undergraduate degree in Statistics from the University of Cape Town and a master's degree in Artificial Intelligence from the University of Southampton.

My current work focuses on developing computer vision techniques to understand live 3D cancer biology. My research interests are in Geometric Deep Learning, 3D Computer Vision, Shape Analysis and Explainable AI.

Please see my organisations - Sentinal4D and MagniViT - for my work on geometric deep learning in cancer biology, and vision transformers for classification of soft tissue sarcoma from whole slide images.

Aside from my work and research, I am an avid runner, swimmer, cyclist, and footballer.

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Matt De Vries's Projects

pytorch-3dunet icon pytorch-3dunet

3D U-Net model for volumetric semantic segmentation written in pytorch

pytorch-enhance icon pytorch-enhance

Open-source Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use

pytorch-gan icon pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

pytorch-unet icon pytorch-unet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

retina-unet icon retina-unet

Retina blood vessel segmentation with a convolutional neural network

rfnet-4d icon rfnet-4d

Code release for ECCV 2022 paper "RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds"

segloss icon segloss

A collection of loss functions for medical image segmentation

shapr icon shapr

SHAPR - An AI approach to predict 3D cell shapes from 2D microscopic images

simclr icon simclr

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.

swiple icon swiple

Swiple enables you to easily observe, understand, validate and improve the quality of your data

tearingnet icon tearingnet

Source code for the paper "TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations"

tiler icon tiler

N-dimensional NumPy array tiling and merging with overlapping, padding and tapering

timesformer icon timesformer

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

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