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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.
Image segmentation and classification for Covid19 lung CT-scans using UNET implemented in Tensorflow and Keras.
This is a project of the Google Developer Student Club of FPT University Da Nang, built by members, and entered the TOP 10 finalists at the FPT Edu Hackathon 2021 in Hanoi, Vietnam.
The repository contains the MATLAB script. The .csv file in the Results folder is used as ground truth for the study of the proposed algorithm.
Segmentation of lungs using 3D CT scan of the patient using 2D U-net procedure