medical-projects's repositories
B-SOID
Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.
bisenetv2-tensorflow
Unofficial tensorflow implementation of real-time scene image segmentation model "BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation"
Breast-cancer-diagnosis-using-KNN
KNN implemented in Python for predictin if a tumor is malignant or benign
Cam2BEV
TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
clinical-outcome-prediction
Code for the EACL 2021 Paper: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
dl4mi-1
Project for "Deep Learning in Medical Imaging" by Prof Greenspan
DLC_ROI_tool
A tool for drawing ROIs on videos and analysing deeplabcut videos.
DST-CBC
Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"
EEG-DL
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
HIVEVO_recombination
Scripts created to analyse recombination in HIVEVO dataset
interpretable-ml-covid-19
Interpretable Machine Learning for COVID-19
KiU-Net-pytorch
Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)
MIMIC_RL_COACH
Clinical decision support - reinforcement learning agent for sepsis treatment in intensive care
MIScnn
A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
mrbreader
Small tool to unpack and prepare MRB (medical reality bundle) files from the 3D Slicer as a supervised deep learning data source.
NAS-MIP
Deep learning using neural architecture search for medical imaging problems
point-transformer-pytorch
Implementation of the Point Transformer layer, in Pytorch
Probabilistic-Deep-Learning-with-TensorFlow
Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.
pyTsetlinMachineParallel
Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.
pyVHR
Python framework for Virtual Heart Rate
rebiber
A simple tool to update bib entries with their official information (e.g., DBLP or the ACL anthology).
resseg
Automatic segmentation of postoperative brain resection cavities.
RTK
Reconstruction Toolkit
SmartHome-DataAnalytics
And awesome project to predict activities base on sensors and bring insides about healthcare
tpu
Reference models and tools for Cloud TPUs.
Unet_Medical_Segmentation
This is an implementation of “U-Net: Convolutional Networks for Biomedical Image Segmentation” and "UNet++: A Nested U-Net Architecture for Medical Image Segmentation" in Python and powered by the Tensorflow2 deep learning framework. Unet family has been proposed for a more precise segmentation on medical image.
UNITOPATHO
Dataset of 9536 H&E-stained patches for colorectal polyps classification and adenomas grading