Response Evaluation Criteria in Solid Tumors
PulseAugur coverage of Response Evaluation Criteria in Solid Tumors — every cluster mentioning Response Evaluation Criteria in Solid Tumors across labs, papers, and developer communities, ranked by signal.
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AI improves cancer lesion segmentation in CT scans using user-guided priors
Researchers have investigated the use of user-prompted priors to improve semi-automated cancer lesion segmentation in whole-body computed tomography scans. The study found that more complex spatial priors consistently e…
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User-prompted priors boost cancer lesion segmentation in CT scans
Researchers have explored how user-provided information, known as priors, can enhance the semi-automated segmentation of cancer lesions in computed tomography scans. The study found that more complex spatial priors, suc…
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LETT-NeXt model enhances 3D CT lesion segmentation with RECIST guidance
Researchers have developed LETT-NeXt, a lightweight model designed for 3D lesion segmentation in CT scans, guided by RECIST markers. This model aims to provide a more comprehensive 3D description of lesion extent compar…
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LETT-NeXt model enhances 3D CT lesion segmentation with RECIST guidance
Researchers have developed LETT-NeXt, a lightweight model designed for segmenting 3D lesions in CT scans. This model incorporates RECIST (Response Evaluation Criteria in Solid Tumors) markers as guidance, enhancing its …