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Name: anindo-saha
Type: User
Company: http://www.diagnijmegen.nl/
Bio: PhD Candidate @DIAGNijmegen.
Twitter: anindox8
Location: Nijmegen, The Netherlands
Name: anindo-saha
Type: User
Company: http://www.diagnijmegen.nl/
Bio: PhD Candidate @DIAGNijmegen.
Twitter: anindox8
Location: Nijmegen, The Netherlands
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.
Fully supervised, multi-class 3D brain segmentation in T1 MRI, using atlas-based segmentation algorithms (label propagation, tissue models, Expectation-Maximization algorithm).
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).
Deep Learning Examples
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.
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.
Fully supervised binary classification of skin lesions from dermatoscopic images using multi-color space moments/texture features and Support Vector Machines/Random Forests.
Baseline AI models for 3D csPCa detection/diagnosis in bpMRI
Unsupervised region proposal and supervised patch extraction algorithms for extracting candidate 2D ROIs to train SVM/CNN classifiers, for mass detection in mammograms.
This repository stores all the content for the diag websites.
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