varun764

varun764's Stars

• VonderTech/Image-Feature-Extractor

  Analyzes the contents of an image by determining features of image regions. The program demonstrates one of the first steps of character recognition in OCR.

  Language:Java1

• misaelljr/Features_Extractors

  Extractors of features of synthetic images of blood vessels in 3D and 2D models.

  Language:Java11

• leonematias/DeepNeuralNetwork

  Deep neural network implemented in Java from scratch, without using library/framework.

  Language:Java135

• rdspring1/LSH_DeepLearning

  Scalable and Sustainable Deep Learning via Randomized Hashing

  Language:Java9122

• Jasonnor/Backpropagation

  Implementing multilayer neural networks through backpropagation using Java.

  Language:Java24666

• wheresvic/neuralnet

  A simple Neural Net implementation with examples

  Language:Java148

• mw827/TADPOLE

  This is for the TADPOLE (The Alzheimer's Disease Prediction Of Longitudinal Evolution) project

  Language:Jupyter Notebook2

• JianfengWu1993/TADPOLE-challenge

  Language:MATLAB1

• 88vikram/TADPOLE_submission_with_debm

  Language:Python12

• mattdns1006/her2

  Language:Jupyter Notebook2

• JenifferWuUCLA/pulmonary-nodules-segmentation

  Tianchi medical AI competition [Season 1]: Lung nodules image segmentation of U-Net. U-Net训练基于卷积神经网络的肺结节分割器

  Language:Jupyter Notebook11149

• JenifferWuUCLA/pulmonary-nodules-MaskRCNN

  Mask R-CNN for Pulmonary Nodules Diagnosis, using TensorFlow 天池医疗AI大赛：Mask R-CNN肺部结节智能检测（Segmentation + Classification）

  Language:Jupyter Notebook21094

• mathewhall100/ISIC-skin-lesion-identification

  Deep learning model for differentiating between malignant melanoma and benign skin lesions in images from the ISIC skin lesion archive. Model achieved 88% accuracy in correctly distinguishing malignant from benign lesions.

  Language:Jupyter Notebook1

• carricky/miccai_challenge_siyuan

  Language:Python1

• pennmem/neurorad_pipeline

  Language:Python43

• Tejas-26/holmuskTest

  Source code for a markdown report of ethnic minority patients with comorbid substance use disorders

  Language:Python1

• dPys/PyNets

  A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

  Language:Python11941

• AlexandrePsq/LePetitPrince

  LePetitPrince project (fMRI & MEG)

  Language:Jupyter Notebook43

• frosinastojanovska/brain_convolution

  Language:Python31

• ThomasYeoLab/Standalone_He2019_KRDNN

  Language:MATLAB21

• alessandrostranieri/icns_adhd_fmri

  Language:Python1

• mturja-vf-ic-bd/2019_CNI_TrainingRelease

  Training data release for Connectomics in NeuroImaging - Transfer Learning Challenge (CNI-TLC). For more details, see http://miccai.brainconnectivity.net

  Language:Python1

• nilearn/nilearn

  Machine learning for NeuroImaging in Python

  Language:Python1.2k591

• phoenixpei/Predicting-Angle-Closure-Glaucoma

  Predicting angle closure glaucoma using different classification models including Logistic Regression, Neural Network, KNN, Decision Tree & Random Forest, Support Vector Machine, etc. Then generating stacked ensemble models based on those methods.

  Language:R1

• SlicerIGT/PostureAssessmentToolkit

  Slicer modules to generate landmarks from US spine segmentation, create visualizations from landmarks, and ensure suitability of landmark sets for visualization

  Language:Python2

• zhumangen/DROC

  Digitial X-Ray Image Manager.

  Language:C++99

• apenaz/pdi-work

  Trabalho da disciplina DCA0445 - Processamento Digital de Imagens Prof. Agostinho Brito

  Language:Python1

• csuzll/Code

  Language:Jupyter Notebook3

• m-albert/quantification

  Framework for accessing and processing microscopy data

  Language:Python3

• raywu0123/Brain-Tumor-Segmentation

  Language:Python73