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Found 5 papers, 2 papers with code
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657, 566 segmentation masks.
Towards unraveling calibration biases in medical image analysis
In recent years the development of artificial intelligence (AI) systems for automated medical image analysis has gained enormous momentum.
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
To this end, we introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures.
Impact of class imbalance on chest x-ray classifiers: towards better evaluation practices for discrimination and calibration performance
This work aims to analyze standard evaluation practices adopted by the research community when assessing chest x-ray classifiers, particularly focusing on the impact of class imbalance in such appraisals.
Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures
We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool.
