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 2 years ago
Alternatives and similar repositories for StreamingCNN
Users that are interested in StreamingCNN are comparing it to the libraries listed below
Sorting:
- Dense Steerable Filter CNN☆77Updated 2 years ago
- This is a PyTorch implementation of the paper: "Processing Megapixel Images with Deep Attention-Sampling Models".☆41Updated last year
- Use streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.☆79Updated 2 years ago
- Segmenting WSIs using Deep Convolutional Neural Networks☆19Updated 7 years ago
- Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands☆111Updated 5 years ago
- Roto-reflection equivariant CNNs for Keras as presented in B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivar…☆86Updated 5 years ago
- A Python module that produces image patches and annotation masks from whole slide images for deep learning in digital pathology.☆80Updated 3 years ago
- Feature Aware Normalization - Code for "Context-based Normalization of Histological Stains using Deep Convolutional Features"☆15Updated 6 years ago
- SlideRunner is a tool for massive cell annotations in whole slide images☆79Updated last month
- Lightweight framework for fast prototyping and training deep neural networks with PyTorch and TensorFlow☆220Updated 4 years ago
- Core functionality of Eisen☆41Updated 4 years ago
- Official code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medic…☆62Updated 3 years ago
- A PyTorch implementation of the Probabilistic U-Net, applied to probabilistic glioma growth☆43Updated 6 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
- Code accompanying the paper Neural Image Compression for Gigapixel Histopathology Image Analysis☆50Updated 2 years ago
- Codes for our MIA paper "DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks".☆24Updated 6 years ago
- WACV2021 - A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images☆40Updated last year
- Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data☆83Updated 3 years ago
- MIDL 2018 / MEDIA 2019: one binary extremely large and inflecting sparse kernel (pytorch)☆44Updated 5 years ago
- CNN based segmentation codes☆47Updated 2 years ago
- PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images☆41Updated 3 years ago
- This is supplementary material for the manuscript: "Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Ne…☆32Updated 7 years ago
- Smooth Loss Functions for Deep Top-k Classification☆257Updated 4 years ago
- Tensorflow Code for "PHiSeg: Capturing Uncertainty in Medical Image Segmentation", Proc. MICCAI 2019☆128Updated 2 years ago
- Repository for the Medical Out-of-Distribution Analysis Challenge.☆63Updated last year
- Self-supervised learning for microscopy images☆55Updated 6 years ago
- Spatial Decomposition and Transformation Network - TensorFlow☆14Updated 5 years ago
- Our solution for ICIAR 2018 Grand Challenge☆193Updated 4 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 regular Tiramisu network for PyTorch with a LOT of extra features!☆42Updated 4 years ago