Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:15:54.783799Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2411.15232.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:15:54.783799Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
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63 of 63 outbound references displayed
External citation measurements
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Observation ed09910c-ca91-4d1b-99e5-0931e84cc3a9 · outbound
BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models GPT-4 Technical Report
Reference 1
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Dataset of breast ultrasound images
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Making the most of text semantics to improve biomedical vision–language processing
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models gscorecam: What objects is clip looking at? In Proceedings of the Asian Conference on Computer Vision , pages 1959– 1975, 2022
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models brain tumor dataset, 2017
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
Reference 10
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models BCN20000: Dermoscopic Lesions in the Wild
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Cleft: Language-image contrastive learning with efficient large language model and prompt fine-tuning
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Does clip benefit visual question answering in the medical domain as much as it does in the general domain?, 2021
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Aligning medical images with general knowl- edge from large language models
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Potential of gpt-4 for de- tecting errors in radiology reports: Implications for reporting accuracy
Reference 17
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Multi-class texture anal- ysis in colorectal cancer histology
Reference 22
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Self-regulating prompts: Foundational model adaptation without forgetting
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Reference 30
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Automatic no- reference quality assessment for retinal fundus images using vessel segmentation, 2013
Reference 31
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Kvasir: A multi-class image dataset for com- puter aided gastrointestinal disease detection
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Reference 57
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