92 research outputs found
The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases
This study investigates the transformative potential of Large Language Models
(LLMs), such as OpenAI ChatGPT, in medical imaging. With the aid of public
data, these models, which possess remarkable language understanding and
generation capabilities, are augmenting the interpretive skills of
radiologists, enhancing patient-physician communication, and streamlining
clinical workflows. The paper introduces an analytic framework for presenting
the complex interactions between LLMs and the broader ecosystem of medical
imaging stakeholders, including businesses, insurance entities, governments,
research institutions, and hospitals (nicknamed BIGR-H). Through detailed
analyses, illustrative use cases, and discussions on the broader implications
and future directions, this perspective seeks to raise discussion in strategic
planning and decision-making in the era of AI-enabled healthcare
Seeing the arrow of time
URL to conference programWe explore whether we can observe Time’s Arrow in a temporal sequence–is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time’s Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards /backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.European Research Council (ERC grant VisRec no. 228180)National Basic Research Program of China (973 Program) (2013CB329503)National Natural Science Foundation (China) (NSFC Grant no. 91120301)United States. Office of Naval Research (ONR MURI grant N00014-09-1-1051)National Science Foundation (U.S.) (NSF CGV-1111415
Deviation magnification: Revealing departures from ideal geometries
Structures and objects are often supposed to have idealized geometries such as straight lines or circles. Although not always visible to the naked eye, in reality, these objects deviate from their idealized models. Our goal is to reveal and visualize such subtle geometric deviations, which can contain useful, surprising information about our world. Our framework, termed Deviation Magnification, takes a still image as input, fits parametric models to objects of interest, computes the geometric deviations, and renders an output image in which the departures from ideal geometries are exaggerated. We demonstrate the correctness and usefulness of our method through quantitative evaluation on a synthetic dataset and by application to challenging natural images.Shell ResearchQatar Computing Research InstituteUnited States. Office of Naval Research (Grant N00014-09-1-1051)National Science Foundation (U.S.) (Grant CGV-1111415
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Automatic rib labeling and anatomical centerline extraction are common
prerequisites for various clinical applications. Prior studies either use
in-house datasets that are inaccessible to communities, or focus on rib
segmentation that neglects the clinical significance of rib labeling. To
address these issues, we extend our prior dataset (RibSeg) on the binary rib
segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT
scans (15,466 individual ribs in total) and annotations manually inspected by
experts for rib labeling and anatomical centerline extraction. Based on the
RibSeg v2, we develop a pipeline including deep learning-based methods for rib
labeling, and a skeletonization-based method for centerline extraction. To
improve computational efficiency, we propose a sparse point cloud
representation of CT scans and compare it with standard dense voxel grids.
Moreover, we design and analyze evaluation metrics to address the key
challenges of each task. Our dataset, code, and model are available online to
facilitate open research at https://github.com/M3DV/RibSegComment: 10 pages, 6 figures, journa
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