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mcv-m6-2019-team1

mdb-ui-kit

Bootstrap 5 & Material Design 2.0 UI KIT

mdm

A TensorFlow implementation of the Mnemonic Descent Method.

mdp_tracking

Reinforcement learning based multi object tracker

mdp_tracking-1

Learning to Track: Online Multi-Object Tracking by Decision Making

mdvc

PyTorch implementation of Multi-modal Dense Video Captioning (CVPR 2020 Workshops)

mean-stack-app-managing-book-store-data

MEAN Stack app of managing book store data

med

Multi-Scale Deep Lung

med-attack

Implementation for paper Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models

med-students-learning-analytics

Cohort 11 Capstone Project for the Certificate of Data Science at Georgetown University School of Continuing Studies.

medgan

Generative adversarial network for generating electronic health records.

mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

medic-docs

Documentation for developing, configuring, and using Medic Mobile tools

medical-appointments-no-shows

Investigate a dataset of appoinment records for Brasil public hospitals

medical-data

medical-datasets

tracking medical datasets, with a focus on medical imaging

medical-image-analysis

Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning and Deformable models

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

medical-imaging-datasets

A list of Medical imaging datasets. Source : https://sites.google.com/site/aacruzr/image-datasets

medical-imaging-semantic-segmentation

This repository contains UNet and ICNet implementations for semantic segmentation of nuclei images, from Kaggle's 2018 Data Science Bowl

medical-report-generation

A pytorch implementation of On the Automatic Generation of Medical Imaging Reports.

medicaldetectiontoolkit

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

medicalzoopytorch

A comparative analysis of multi-modal brain-MRI segmentation with 3D deep neural networks

medium-articles

Repo for all my code on the articles I post on medium

medium-clustering

Inter-Class Clustering of Text Data Using Dimensionality Reduction and BERT

medium-editor

Medium.com WYSIWYG editor clone. Uses contenteditable API to implement a rich text solution.

medium-facenet-tutorial

Facial Recognition Pipeline using Dlib and Tensorflow

medium-intelligence

Medium documentations, articles, scripts, write ups and tutorials

medium-show-and-tell-caption-generator

Code to run inference on a Show And Tell Model. This model is trained to generate captions given an image.

medium_blogs_analytics

Understanding the factors impacting number of claps on medium blogs - medium.com