Topic: lung-disease Goto Github
Some thing interesting about lung-disease
Some thing interesting about lung-disease
lung-disease,Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).
User: anindox8
lung-disease,AirQuant is a framework based in MATLAB primarily for extracting airway measurements from fully segmented airways of a chest CT.
User: ashkanpakzad
Home Page: https://airquant.readthedocs.io/
lung-disease,Labelless automated airway measurement using style transfer to generate synthetic data.
User: ashkanpakzad
Home Page: https://ashkanpakzad.github.io/ATN/
lung-disease,ICVGIP' 18 Oral Paper - Classification of thoracic diseases on ChestX-Ray14 dataset
User: ekagra-ranjan
lung-disease,Lung diseases classification in 2D using chest CT cases and Analysis the multi-channel effect on classification. This work is been done during summer internship July-Aguest 2018, Duke University Medical Center.
User: fitushar
lung-disease,This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)
User: fitushar
lung-disease,Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data
Organization: frankkramer-lab
lung-disease,My 34th place solution to the OSIC Pulmonary Fibrosis Progression Competition hosted on Kaggle π¬
User: greatgamedota
Home Page: https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression/
lung-disease,The objective of this project is to develop a model utilizing a convolutional neural network (CNN) for the classification of lung infections in individuals based on medical imagery.
User: jigisha-p
lung-disease,Automated lung segmentation in CT
User: johof
lung-disease, The COVID-19 virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, so itβs important that you also practice respiratory etiquette (for example, by coughing into a flexed elbow). Deep learning can be used to detect COVID-19 in a patient as recent studies has shown that people suffering from covid19 has infectiuos lung diseases. So in this project I am using deep learning to detect CoronaVirus using chest X-ray.
User: kanishksh4rma
lung-disease,A system to provide Health care for patients, Doctors and Radiologists
User: kareem-mohamed-wardany
lung-disease,Deep learning Project on Pneumonia Classification
User: mastermind2001
lung-disease,Lung segmentation for chest X-Ray images with ResUNet and UNet. In addition, feature extraction and tuberculosis cases diagnosis had developed.
User: mo26-web
lung-disease,The preparation for the Lung X-Ray Mask Segmentation project included the use of augmentation methods like flipping to improve the dataset, along with measures to ensure data uniformity and quality. The model architecture was explored with two types of ResNets: the traditional CNN layers and Depthwise Separable.
User: mohammad95labbaf
lung-disease,Image classification: binary classification of Lung X-ray grayscale images using ChexNet
User: nivation
lung-disease,Motion-compensated low-rank reconstruction for simultaneous structural and functional UTE lung MRI doi: 10.1002/mrm.29703
Organization: pulmonarymri
lung-disease,MDPD - Microbiome Database of Pulmonary Diseases
Organization: pulmonomicslab
Home Page: http://bicresources.jcbose.ac.in/ssaha4/mdpd/
lung-disease,This repo contains the source code of my undergraduate thesis project.
User: rafi-fauzan
lung-disease,Pleural Effusion Classifier Model PyTorch
User: rafi-fauzan
lung-disease,This is a 3D Slicer extension for segmentation and spatial reconstruction of infiltrated, collapsed, and emphysematous areas in lung CT.
User: rbumm
lung-disease,The website of VN AIDr project
Organization: vnopenai
Home Page: https://vnopenai.github.io/ai-doctor/
lung-disease,Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung disease that causes lung function decline by lung tissue scarring. Although lung function decline is assessed by the forced vital capacity (FVC), determining the accurate progression of IPF remains a challenge. To address this challenge, we proposed Fibro-CoSANet, a novel end-to-end multi-modal learning-based approach, to predict the FVC decline. Fibro-CoSANet utilized CT images and demographic information in convolutional neural network frameworks with a stacked attention layer. Extensive experiments on the OSIC Pulmonary Fibrosis Progression Dataset demonstrated the superiority of our proposed Fibro-CoSANet by achieving the new state-of-the-art modified Laplace Log-Likelihood score of -6.68. This network may benefit research areas concerned with designing networks to improve the prognostic accuracy of IPF.
User: zabir-nabil
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