Development of U-Nets capable of segmenting Brain Lower Grade Glioma MRI images. Integrating standard U-Net with transformers blocks
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Updated
Mar 20, 2023 - Jupyter Notebook
Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis, and medical intervention.
Development of U-Nets capable of segmenting Brain Lower Grade Glioma MRI images. Integrating standard U-Net with transformers blocks
This project compares U-Net, FCN, and DeepLabv3 models for instance-level lung image segmentation
Ultrasound Image Recognition
Image-processing application
Pneumonia identification from chest x-ray images using deep learning algorithms Used transfer learning techniques to develop an artificial intelligence system.
Determine whether the Brain MRI image has a tumour. It also segments the brain image.
Hippocampus segmentation
🌐 MIDA Project Webpage
PETRA: Matlab Toolbox for brain image processing and classification.
The Pytorch implementation of the 3D Anisotropic Hybrid Network described in the paper "3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes"
This project aims to perform binary classification to detect presence of cancerous cells in histopathological scans. The images are taken from the histopathological scans of lymph node sections from Kaggle Histopathological cancer detection challenge and provide tumor visualizations of tumor tissues.
Code repo for the Kaggle Challenge from the MVA class Deep Learning for Medical Imaging
Using Convolutional Nerual Networks in Knee Cartilage Segmentation from Ultrasound Images
Classification of x-ray images using grid approach : Image Processing Lab Project
The project has been developed for the exam of the "Image Processing and Computer Vision" course at University of Bologna. The evaluation of the project led to the maximum grade..
SAM on medical images based on https://github.com/facebookresearch/segment-anything
JointNET is a deep learning model designed to predict active inflammation in sacroiliac joints using radiographs. Developed using a dataset of 1,537 grade 0 SIJs, the model showcases superior accuracy compared to human observers. This repository contains the code used in the development and validation of JointNET.