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Found 9 papers, 2 papers with code
Decomposing Vision-based LLM Predictions for Auto-Evaluation with GPT-4
The volume of CT exams being done in the world has been rising every year, which has led to radiologist burn-out.
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Additionally, using these improved annotations in classification supervision, we demonstrate substantial advancements in model quality, with an increase of 1. 7 pp in AUROC over models trained with annotations from the state-of-the-art approach.
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize.
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging.
Semantic Image Synthesis for Abdominal CT
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset.
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
To ascertain potential biases in algorithm performance in this context, we curated the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones.
Disparities in Dermatology AI: Assessments Using Diverse Clinical Images
AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of diverse skin tones or uncommon diseases.
