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Jason Rudy (jcrudy)

jcrudy

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Location:San Francisco

Home Page:http://www.modusdatascience.com

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Jason Rudy's starred repositories

Multi-Scale-Attention

Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

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MIScnn

A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

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deepvesselnet

Implementation of the DeepVesselNet deep learning network

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itk-vtk-viewer

2D / 3D web image, mesh, and point set viewer using itk-wasm and vtk.js

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pyvista

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

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ml-pyxis

Tool for reading and writing datasets of tensors in a Lightning Memory-Mapped Database (LMDB). Designed to manage machine learning datasets with fast reading speeds.

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mmagic

OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.

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finegan

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-grained Object Generation and Discovery

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ANTs

Advanced Normalization Tools (ANTs)

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gan

Tooling for GANs in TensorFlow

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accel-brain-code

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

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KiU-Net-pytorch

Official Pytorch Code of KiU-Net for Image/3D Segmentation - MICCAI 2020 (Oral), IEEE TMI

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trax

Trax — Deep Learning with Clear Code and Speed

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FewShot_GAN-Unet3D

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

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SegLossOdyssey

A collection of loss functions for medical image segmentation

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awesome-panoptic-segmentation

Panoptic Segmentation Resources List

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3D-cGAN

3D conditional GAN for medical image segmentation

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Brain_Tumour_Segmentation

Code for training a 3DUnet for Brain tumour segmentation from Brats 2019 dataset; for feature extraction from the segmented volumes and for survival prediction. Run train.py for training, segment.py for segmenting test scans and evaluate.py for evaluating the performance of those segmentations. Basic code also written to perform survival prediction with a random forest classifiier.

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dicomgenerator

Create DICOM images for use in testing. Based on pydicom

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node-dicom-faker

Generate random dicom header for testing purposes

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BadMedicine.Dicom

CLI / Library for generating dicom files for use in testing applications. Images generated have 'realistic' tag data (based on aggregated tag data in dicom images taken in Scotland).

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mamba

The Fast Cross-Platform Package Manager

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pyImSegm

Image segmentation - general superpixel segmentation & center detection & region growing

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Active-Contour-Loss

Implementation of active contour loss function

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SegWithDistMap

How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

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unet-vessel-segmentation

Retinal blood vessel segmentation using a tiny U-Net model

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keras-contrib

Keras community contributions

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keras-crf-layer

Implementation of CRF layer in Keras.

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imgaug

Image augmentation for machine learning experiments.

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horovod

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

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