kristinakupf Goto Github PK

bach_proj

A classification model train on BreAst Cancer Histology (BACH).

deepcontext

Author's implementation of 'Unsupervised Visual Representation Learning by Context Prediction'

exemplar-cnn

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

featurelearningrotnet

forecasting-bootcamp

Vector forecasting bootcamp demo

hiexpan

The source code used for automatic taxonomy construction method HiExpan, published in KDD 2018

histo_strengthindiversity

Code for Strength in Diversity: Understanding the impacts of diverse training sets in self-supervised pre-training for histology images

jigsawpuzzlepytorch

Pytorch implementation of the method from the paper "Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles"

mentalhealth_forecasting

Forecasting for mental health service utilization

midllatextemplate

Latex template for the MIDL Conference

pcam

The PatchCamelyon (PCam) deep learning classification benchmark.

pcam_depracated

Training a Breat Cancer Classifier on PatchCamelyon dataset

py-wsi

Python package for dealing with whole slide images (.svs) for machine learning, particularly for fast prototyping. Includes patch sampling and storing using OpenSlide. Patches may be stored in LMDB, HDF5 files, or to disk. It is highly recommended to fork and download this repository so that personal customisations can be made for your work.

revisiting-self-supervised

setexpan

The source code for SetExpan framework, published in ECML-PKDD 2017

slideseg

A Python module that produces image patches and annotation masks from whole slide images for deep learning in digital pathology.

unet

unet for image segmentation

vision

Datasets, Transforms and Models specific to Computer Vision