sixitingting

shoulder-c

PyTorch Implementation for Classification of Fracture/Normal Shoulder Bone X-ray Images

pyod

(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

3d-mri-brain-tumor-segmentation-using-autoencoder-regularization

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).

deepbrain

Deep Learning tools for brain medical images

MONAI-course

MNAD

An official implementation of "Learning Memory-guided Normality for Anomaly Detection" (CVPR 2020) in PyTorch.

MahalanobisAD-pytorch

PyTorch implementation of "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection"

P_Net_Anomaly_Detection

This is the implementation of our paper in ECCV 2020.

ResNetVAE

Variational AutoEncoder + ResNet Transfer Learning

segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

xray

Unsupervised Anomaly Detection for X-Ray Images

rsfMRI_VAE

#work in progress

memAE

unofficial implementation of paper Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder (MemAE) for Unsupervised Anomaly Detection

v2rayN

MeshPooling

Code for 'Mesh Variational Autoencoders with Edge Contraction Pooling'

gaussian-ad-mvtec

Code underlying our publication "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection" at ICPR2020

slices-to-3d-brain-vae

Code accompanying "Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE"

Unsupervised_Anomaly_Detection_Brain_MRI

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

connected-components-3d

Connected components on multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants.

simpleBAD

Reconstruction-by-inpainting-for-visual-anomaly-detection

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

tutorials-1

MONAI Tutorials

deviation-network

Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection

skip-ganomaly

Source code for Skip-GANomaly paper

ganomaly

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

MAMA_NET_Pytorch

anomaly_detection

This is the official implementation of "Anomaly Detection with Deep Perceptual Autoencoders".

SegLoss

A collection of loss functions for medical image segmentation

boundary-loss

Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.

home-assignment