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josedolz / LiviaNET

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This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

deep-neural-networks theano deep-learning architecture cnn mri medical-imaging neural-networks deep-learning-algorithms neuroimaging convolutional-networks convolutional-neural-networks image-segmentation nifti-format neuroimage medical-image-processing 3d-convolutional-network 3d-cnn scale-study medical-image-analysis medical-image-segmentation brain-segmentation
  • Updated Nov 20, 2019
  • Python

josedolz / HyperDenseNet

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This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.

deep-neural-networks theano deep-learning cnn neural-networks deep-learning-algorithms segmentation densenet fcn convolutional-neural-networks weight image-segmentation nifti-format epoch medical-image-processing 3d-convolutional-network 3d-cnn medical-image-analysis medical-image-segmentation brain-segmentation hyperdensenet multi-modal-imaging segment-medical-images
  • Updated Nov 20, 2019
  • Python

BubblyYi / Coronary-Artery-Tracking-via-3D-CNN-Classification

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The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

deep-learning pytorch centerline centerline-detection vessel medical-image-processing 3d-cnn 3d-classification coronary-artery centerline-extraction 3d-cnn-tracker blood-vessel
  • Updated Apr 14, 2021
  • Python

DeepRank / deeprank

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Performance of _get_relevance()

NicoRenaud
NicoRenaud commented Oct 27, 2020

The function _get_relevance of NeuralNet (that is called at each epoch) is really slow and takes up to a few hours on cartesius when training on 001-003 of BM5. In comparison the training during the epoch takes about 2 hours .This is due to the fact that for each molecule we open the hdf5, read the irmsd and close the hdf5. We could instead preload the irmsd during the data preprocessing when

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enhancement good first issue

fitushar / WeaklySupervised-3D-Classification-of-Chest-CT-using-Aggregated-MultiResolution-Segmentation-Feature

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This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)

deep-learning multi-label-classification tfrecords 3d multi-resolution weakly-supervised-learning 3d-cnn segmentation-models tensorflow2 ct-imaging segmentation-features lung-disease feature-aggregation densevnet3d spie-cad weighted-loss weakly-supervised-classification multi-label-loss weight-binary-crossentrophy spie-cad-2020
  • Updated Jun 18, 2020
  • Python

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