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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

3dgan_pytorch icon 3dgan_pytorch

Implementation of 3D GAN in PyTorch based on http://3dgan.csail.mit.edu/papers/3dgan_nips.pdf

3dgcn icon 3dgcn

Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis

acsconv icon acsconv

[IEEE JBHI] Reinventing 2D Convolutions for 3D Images - 1 line of code to convert pretrained 2D models to 3D!

agnet icon agnet

The code of "Attention Guided Network for Retinal Image Segmentation" in MICCAI 2019

aistplusplus_api icon aistplusplus_api

API to support AIST++ Dataset: https://google.github.io/aistplusplus_dataset

attention-gated-networks icon attention-gated-networks

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

attentiondeepmil icon attentiondeepmil

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

causalbench-starter icon causalbench-starter

Starter repository for submissions to the CausalBench challenge for gene-gene graph inference from genetic perturbation experiments.

cell-segmentation icon cell-segmentation

PyTorch implementation of several neural network models for cellular image segmentation

cell-tracking icon cell-tracking

Python scripts for tracking cells in fluorescent microscopy.

celldiffusion icon celldiffusion

Denoising Defusion for 3D images of cells, conditioned on ground truth shape features.

cellshape icon cellshape

3D shape analysis of cancer cells using deep learning

chamferdist icon chamferdist

Pytorch package to compute Chamfer distance between point sets (pointclouds).

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