DIAGNijmegen / StreamingCNNLinks
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.
☆100Updated last year
Alternatives and similar repositories for StreamingCNN
Users that are interested in StreamingCNN are comparing it to the libraries listed below
Sorting:
- Use streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.☆80Updated 2 years ago
- This is a PyTorch implementation of the paper: "Processing Megapixel Images with Deep Attention-Sampling Models".☆41Updated last year
- Dense Steerable Filter CNN☆75Updated 2 years ago
- Tensorflow Code for "PHiSeg: Capturing Uncertainty in Medical Image Segmentation", Proc. MICCAI 2019☆127Updated 2 years ago
- PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images☆41Updated 3 years ago
- Code accompanying the paper Neural Image Compression for Gigapixel Histopathology Image Analysis☆50Updated 2 years ago
- Implementation of PyTorch-based multi-task pre-trained models☆33Updated 4 years ago
- Syntax - the arrangement of whole-slide-images and their image tiles to create well-formed computational pathology pipelines.☆55Updated 2 years ago
- Repository for the Medical Out-of-Distribution Analysis Challenge.☆63Updated 11 months ago
- Official code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medic…☆62Updated 3 years ago
- A Python module that produces image patches and annotation masks from whole slide images for deep learning in digital pathology.☆78Updated 3 years ago
- CNN based segmentation codes☆47Updated 2 years ago
- Lightweight framework for fast prototyping and training deep neural networks with PyTorch and TensorFlow☆220Updated 4 years ago
- Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands☆110Updated 5 years ago
- This Python package enables the training and inference of deep learning models for very large data, such as megapixel images, using atten…☆97Updated 5 years ago
- A PyTorch implementation of the Probabilistic U-Net, applied to probabilistic glioma growth☆43Updated 5 years ago
- Repository for the article "Unsupervised domain adaptation for medical imaging segmentation with self-ensembling".☆43Updated 6 years ago
- Keras w/ Tensorflow backend implementation for 3D channel-wise convolutions☆68Updated 5 years ago
- Useful functions to work with PyTorch. At the moment, there is a function to work with cross validation and kernels visualization.☆67Updated 5 years ago
- Segmenting WSIs using Deep Convolutional Neural Networks☆19Updated 6 years ago
- ☆95Updated 6 years ago
- Our solution for ICIAR 2018 Grand Challenge☆193Updated 4 years ago
- ☆84Updated 6 years ago
- MIDL 2018 / MEDIA 2019: one binary extremely large and inflecting sparse kernel (pytorch)☆44Updated 5 years ago
- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation☆55Updated last year
- Datasets, Transforms and Utilities specific to Biomedical Imaging☆110Updated 6 years ago
- Attention-based Deep MIL implementation and application☆156Updated 3 years ago
- Stain normalization parameters used in the paper "F. Ciompi et al., The importance of stain normalization in colorectal tissue classifica…☆28Updated 6 years ago
- H&E tailored Randaugment: automatic data augmentation policy selection for H&E-stained histopathology.☆54Updated 2 years ago
- Core functionality of Eisen☆41Updated 4 years ago