anindox8/Deep-Segmentation-Features-for-Weakly-Supervised-3D-Disease-Classification-in-Chest-CT anindox8/Deep-Segmentation-Features-for-Weakly-Supervised-3D-Disease-Classification-in-Chest-CT

  • Stars
    star
    29
  • Rank 860,307 (Top 17 %)
  • Language
    Python
  • Created about 5 years ago
  • Updated about 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
anindox8/Deep-Segmentation-Features-for-Weakly-Supervised-3D-Disease-Classification-in-Chest-CT
github

anindox8

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).

More Repositories

1

Ensemble-of-Multi-Scale-CNN-for-Dermatoscopy-Classification

Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.
Jupyter Notebook
38
star
2

Atlas-Based-3D-Brain-Segmentation-in-T1-MRI

Fully supervised, multi-class 3D brain segmentation in T1 MRI, using atlas-based segmentation algorithms (label propagation, tissue models, Expectation-Maximization algorithm).
Batchfile
7
star
3

Multi-Color-Space-Features-for-Dermatoscopy-Classification

Fully supervised binary classification of skin lesions from dermatoscopic images using multi-color space moments/texture features and Support Vector Machines/Random Forests.
Jupyter Notebook
7
star
4

Ensemble-of-Multi-Scale-CNN-for-3D-Brain-Segmentation

Fully supervised, multi-class 3D brain segmentation in T1 MRI using an ensemble of diverse CNN architectures (3D FCN, 3D U-Net) with multi-scale input.
Python
4
star
5

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

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.
Python
3
star
6

Region-Proposal-for-Mass-Detection-in-Mammograms

Unsupervised region proposal and supervised patch extraction algorithms for extracting candidate 2D ROIs to train SVM/CNN classifiers, for mass detection in mammograms.
Jupyter Notebook
2
star