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Found 6 papers, 4 papers with code
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
Unlike existing datasets that include a raw sequence of gaze alongside a report, with significant misalignment between gaze location and report content, our FG-CXR dataset offers a more grained alignment between gaze attention and diagnosis transcript.
GazeSearch: Radiology Findings Search Benchmark
After refining the existing eye-tracking datasets, we transform them into a curated visual search dataset, called GazeSearch, specifically for radiology findings, where each fixation sequence is purposefully aligned to the task of locating a particular finding.
Multimodal Learning and Cognitive Processes in Radiology: MedGaze for Chest X-ray Scanpath Prediction
However, predicting human scanpaths on medical images presents unique challenges due to the diverse nature of abnormal regions.
Enhancing Radiological Diagnosis: A Collaborative Approach Integrating AI and Human Expertise for Visual Miss Correction
This study aimed to develop a collaborative AI system, CoRaX, which integrates eye gaze data and radiology reports to enhance diagnostic accuracy in chest radiology by pinpointing perceptual errors and refining the decision-making process.
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks
The network then uses the activation maps to focus on regions of interest and combines both coarse and fine-grained representations of the data.
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
