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Found 10 papers, 8 papers with code
Multi-Image Visual Question Answering for Unsupervised Anomaly Detection
To the best of our knowledge, we are the first to leverage a language model for unsupervised anomaly detection, for which we construct a dataset with different questions and answers.
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision.
Anatomy-Driven Pathology Detection on Chest X-rays
Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions.
Unlocking the Diagnostic Potential of ECG through Knowledge Transfer from Cardiac MRI
In a qualitative analysis, we demonstrate that our learned ECG embeddings incorporate information from CMR image regions of interest.
Interpretable 2D Vision Models for 3D Medical Images
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success.
Interactive and Explainable Region-guided Radiology Report Generation
While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability.
Ranked #1 on
Medical Report Generation
on MIMIC-CXR
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images
To tackle this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for evaluating algorithms for pathology detection in medical images, especially in chest X-rays.
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images
Image-text contrastive learning has proven effective for pretraining medical image models.
Joint Learning of Localized Representations from Medical Images and Reports
Contrastive learning has proven effective for pre-training image models on unlabeled data with promising results for tasks such as medical image classification.
Rotation-Equivariant Deep Learning for Diffusion MRI
So far, they have been proposed for 2D and 3D data.
