medical-projects Goto Github PK

ds-net

Rank 1st in the public leaderboard of SemanticKITTI Panoptic Segmentation (2020-11-16)

dshealth2019_loinc_embeddings

Code and Word2Vec embeddings of LOINC codes for KDD 2019 DSHealth paper "Evaluation of Embeddings of Laboratory Test Codes for Patients at a Cancer Center": https://arxiv.org/abs/1907.09600

dst-cbc

Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

dtan

Official PyTorch implementation for our NeurIPS 2019 paper, Diffeomorphic Temporal Alignment Nets. TensorFlow\Keras version is available at tf_legacy branch.

duecredit

Automated collection and reporting of citations for used software/methods/datasets

dwp

Pytorch implementation of the DWP with application to MRI segmentation

dwv

DICOM Web Viewer: open source zero footprint medical image viewer.

e-health-care

The web app which is used to remove the dependencies on the doctors, to help out the poor and helpless people with the normal medical checkup and to help people avoid paying huge amount to doctor unnecessarily. This project is made with Django, machine learning algorithms and deep learning (ANN and CNN).

e6040-super-resolution-project

Implementation in pytorch of a recent paper in deep adversarial learning for super resolution of medical images.

early-hospital-mortality-prediction-using-vital-signals

The source code of "Early Hospital Mortality Prediction using Vital Signals"

ebm-nlp

Implementation of Baseline model for PICO 2018 dataset https://github.com/bepnye/EBM-NLP/

ecg

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

ecg-arrhythmia-classification

ECG arrhythmia classification using a 2-D convolutional neural network

ecg-classification

Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database

ecg-classification-1

ECG Arrhythmia classification using CNN

ecg-classification-2

ecg-heartbeat-categorization

deep learning to obtain medical inferences from ECG data :chart_with_upwards_trend:

ecg-heartbeat-classification

Human heart arrhythmia classification based on the MIT-BIH ECG data set with random Fourier feature GLM and kernel parameter estimation.

ecg-heartbeat-classification-seq2seq-model

Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

ecg-mit-bih-ae

ecg-wave-classification

(WiP) Repository for a deep learning project built during the application process for Powerful Medical, a Bratislava-based medical AI startup.

ecg_heartbeat_classification

CNN for heartbeat classification

ecg_segmentation

разметка ЭКГ нейросетью

ecgclassifier

CNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)

ecgneuralnetwork

ecir-2018

Medical Forum Question Classification using Deep Learning

ee8204---real-time-multi-task-learning

Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations Implementation

eeg-dl

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

eeg_classification

EEG Sleep stage classification using CNN with Keras

effective-diagnosis-of-breast-cancer-using-knn-algorithm

Early prediction and diagnosis of Breast cancer can prevent its spread and can also aid in effective treatment or medication. Predicting Breast cancer can be a very arduous task as the data can be highly Non-linear and may require high level computation modelling. However, many machine learning algorithms like KNN, K-Means, Decision Trees, Neural Networks etc. have proved to be effective in predicting Breast cancer. This study shows the using of K Nearest Neighbor (KNN) and K-Means algorithm to predict whether a person is having Breast cancer or not using a machine learning model trained with different features. Thus, we inferred that we can predict the Breast Cancer with reasonable accuracy.