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augmented-volumetric-image-generator

Customised Keras' ImageDataGenerator for 3D volumetric medical image

covid-19-research-articles

Covid-19 Research Articles - Explore Text Mining Tools

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-practice

Using RCC Midway2 cluster

deep-unet-for-satellite-image-segmentation

Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet

deep_learning_prostatex

Scripts for running ProstateX deep learning experiments

divyaravindran007007

Config files for my GitHub profile.

fraud-detection-of-mobile-platform-transaction-data

Develop machine learning models to accurately detect and intercept frauds in mobile transactions

imagepy

Image process framework based on plugin like imagej, it is esay to glue with scipy.ndimage, scikit-image, opencv, simpleitk, mayavi...and any libraries based on numpy

keras-io

Keras documentation, hosted live at keras.io

keras-preprocessing

Utilities for working with image data, text data, and sequence data.

kickstarter

Exploration & Predict Project Success

life-expectancy-and-socioeconomic-status-in-american-states

Hands on with MySQL, GCP and Tableau

mp-mri-prostate

prostate-segmenter

Prostate gland segmentation with deep Convolutional Neural Networks

prostate_segmentation

NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures

prostatex-2

SPIE-AAPM-NCI PROSTATEx Challenges-The PROSTATEx Challenge (" SPIE-AAPM-NCI Prostate MR Classification Challenge”) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 SPIE Medical Imaging Symposium. PROSTATEx ran from November 21, 2016 to January 15, 2017, though a "live" version has also been established at https://prostatex.grand-challenge.org which serves as an ongoing way for researchers to benchmark their performance for this task. The PROSTATEx-2 Challenge (" SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge" ) was focused on the development of quantitative multi-parametric MRI biomarkers for the determination of Gleason Grade Group in prostate cancer.

shared-bikes-optimization

Group project aimed at optimizing shared bikes allocation across stations for the city of Chicago

tensorflow

An Open Source Machine Learning Framework for Everyone

workshop_image_segmentation

This repo contains the documentation for the workshop "Understanding Image Segmentation"