Jim Winkens's starred repositories
pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
batchgenerators
A framework for data augmentation for 2D and 3D image classification and segmentation
AttentionDeepMIL
Implementation of Attention-based Deep Multiple Instance Learning in PyTorch
probabilistic_unet
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
deep_Mahalanobis_detector
Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".
StainTools
Tools for tissue image stain normalisation and augmentation in Python 3
Probabilistic-Unet-Pytorch
A Probabilistic U-Net for segmentation of ambiguous images implemented in PyTorch
Confident_classifier
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
StreamingCNN
To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of the input data. Here we demonstrate a method to train convolutional neural networks while holding only parts of the image in memory.
keras-imaging
A Keras package for biological and medical imaging