Carloni, G., Tsaftaris, S. A., & Colantonio, S. (2024). CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning @ MICCAI 2024 UNSURE Workshop

This is the implementation of the visual model mentioned in our paper 'Automated Radiology Report Generation using Conditioned Transformers'.

ChestMultiVision harnesses a ResNet50V2 architecture, trained on the Chest X-ray-14 dataset. It predicts six different findings detectable on chest x-rays, that are: Atelectasis, Effusion, Infiltration, Mass, No Finding, and Nodule.

Convolutional Neural Network for Pneumonia and Covid-19 Detection in Chest X-Ray Images.

CXRMate: Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation

Lung segmentation in chest X-ray images using U-Net

Nextjs Classification of chest x-ray images with pneumonia and normal using dataset kaggle Chest X-Ray Images (Pneumonia), converting to .h5 model to tensorflowjs that is .json and .bin

[CMPB 2024] Attentional Decoder Networks for Chest X-ray Image Recognition on High-resolution Features

The Chest Cancer Classification project diagnoses chest cancer from medical images using deep learning. It integrates MLflow for experiment tracking, DVC for version control, and Flask for backend processing. Docker and a CI/CD pipeline with GitHub Actions and AWS.

一个用于肺炎图像分类的轻量级ResNet18-SAM模型实现，采用SH-DCGAN生成少类样本数据，解决了数据不平衡的问题，同时结合剪枝策略实现轻量化！MedGAN-ResLite-V2 Released! Stay tuned!❤

This project implements federated learning using a ResNet-34 model to classify chest X-ray images into various medical conditions. By distributing the training process across multiple clients holding local datasets, the approach ensures data privacy and leverages the power of decentralized learning.

This repository hosts code for a deep learning project focused on classifying chest X-ray images into normal and abnormal categories, with a specific emphasis on detecting COVID-19 and pneumonia cases. Leveraging convolutional neural networks (CNNs) and transfer learning methodologies, the project aims to achieve precise classification outcomes.

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

Identifying and segmenting pneumothorax pulmonary disease in chest x-rays. Codes and data related to SIIM-ACR Pneumothorax Segmentation Kaggle Challenge.

ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification

An interactive visualization of machine learning model the modified vision transformer (under our research) on COVID-19 chest X-ray dataset (Sync display Revised with Dr. Hocking)

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.

[CVPR 2023] Deep Feature In-painting for Unsupervised Anomaly Detection in X-ray Images

Website for data science undergraduate capstone project "Improving Performance of Vision Encoding Large Language Models with Contextual Prompts" @UCSD HDSI 2024

This repository contains code from our comparative study on state of the art unsupervised pathology detection and segmentation methods.