medical-projects Goto Github PK

interpretingmedicalml

A personal project that involves using gradCAM to understand medical deep learning models as well permutation method combined with SHAP values for determining feature important of medical random forest models.

intracranial-hemorrhage-detection

The objective of this project is to perform multi-label image classification on a medical image dataset using popular deep learning architectures. We detect acute intracranial hemorrhage and its subtypes. The dataset is provided by the Radiological Society of North America(RSNA).

intro

Introduction to Deep Learning for Medical Researchers

isbi2018-diagnostic-classification-of-lung-nodules-using-3d-neural-networks

Network Architecture for the ISBI_2018 paper : DIAGNOSTIC CLASSIFICATION OF LUNG NODULES USING 3D NEURAL NETWORKS

iseeu

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

iseeu2

ISeeU2: visually interpretable ICU mortality prediction using deep learning and free-text medical notes

itk-snap-dl

medical image processing project based on open source software itk-snap and Deep Learning classifiers

jalali-lab-implementation-of-raisr

Implementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.

jama16-retina-replication

JAMA 2016; 316(22) Replication Study

jnns_lim

A transfer learning method of Deep Convolutional Neural Network for medical image recognition

joint_registration_tumor_segmentation

kamp-net

Deep learning for mortality prediction from low-dose CT images

keras-applications

Reference implementations of popular deep learning models.

keras-uncertainty

Utilities to perform Uncertainty Quantification on Keras Models

kinematik

a module for kinematic analysis of deeplabcut outputs

kit19

kits19

The official repository of the 2019 Kidney and Kidney Tumor Segmentation Challenge

kiu-net-pytorch

Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)

kpconv

Kernel Point Convolutions

kpconv-pytorch

Kernel Point Convolution implemented in PyTorch

kpconv.pytorch

PyTorch reimplementation for "KPConv: Flexible and Deformable Convolution for Point Clouds" https://arxiv.org/abs/1904.08889

kpconv_for_dales

krcctumor

krisp

visualization repository for the 2019 nextstrain workshop at KRISP

kuopio-university-hospital-microsurgery-department-visualize-interactive-software

The Visualize Interactive is a desktop software developed to visualize physiological signals during the activities called mesh alignment, knotting, and go-around which are done by the participants of the research team who participated in the synchronization and analysis of the biomarkers under noise and stress. You can download and install the application by using the following link, https://drive.google.com/drive/folders/1ZKrVuZ17Yat7EvErFob9E1PCWSaoPHpm

labelshiftexperiments

Label shift experiments

le_doc

The basic use case of this app is in the field of medical image analysis. With the help of various advances in the field of deep learning we wish to assist in the medical field.

light-weight-refinenet

Light-Weight RefineNet for Real-Time Semantic Segmentation

lipophilicity-prediction

Code for "Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation" paper. Machine Learning for Molecules Workshop @ NeurIPS 2020

liteseg

Pytorch implementation for LiteSeg