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DIAGNijmegen / prostateMR_3D-CAD-csPCa

Star 39

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for voxel-level clinically significant prostate cancer detection in multi-channel 3D bpMRI scans.

mri-images computer-aided-detection attention-mechanisms prostate-cancer computer-aided-diagnosis seresnet tensorflow-keras unetplusplus attention-unet probabilistic-unet
  • Updated Feb 11, 2022
  • Python
TF-1D-2D-ResNetV1-2-SEResNet-ResNeXt-SEResNeXt

Sakib1263 / TF-1D-2D-ResNetV1-2-SEResNet-ResNeXt-SEResNeXt

Star 38

Models supported: ResNet, ResNetV2, SE-ResNet, ResNeXt, SE-ResNeXt [layers: 18, 34, 50, 101, 152] (1D and 2D versions with DEMO for Classification and Regression).

demo machine-learning deep-learning pipeline jupyter-notebook python3 resnet regression-models resnext 2d-convolution keras-tensorflow classification-model 1d-convolution seresnet seresnext tensorflow2
  • Updated Jan 27, 2022
  • Jupyter Notebook

anindox8 / 2D-RetinaNet-for-Prostate-Localization-in-mpMRI

Star 3

Fully supervised, healthy/malignant prostate detection in multi-parametric MRI (T2W, DWI, ADC), using a modified 2D RetinaNet model for medical object detection, built upon a shallow SEResNet backbone.

deep-learning supervised-learning adc convolutional-neural-networks dwi prostate retinanet prostate-cancer tensorflow-estimator seresnet 2d-detection mri-data tensorflow-keras t2w
  • Updated Sep 30, 2020
  • Python

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