MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting | Paper Citation Record

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This paper cites Merlin: A vision language foundation model for 3d computed tomography.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Merlin: A vision language foundation model for 3d computed tomography

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This paper cites A vision–language foundation model for the generation of realistic chest x-ray images.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting A vision–language foundation model for the generation of realistic chest x-ray images

Reference 2

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This paper cites Making the most of text semantics to improve biomedical vision–language processing.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Making the most of text semantics to improve biomedical vision–language processing

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This paper cites Understanding and confronting our mis- takes: the epidemiology of error in radiology and strategies for error reduction.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Understanding and confronting our mis- takes: the epidemiology of error in radiology and strategies for error reduction

Reference 4

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This paper cites Joint modeling of chest radiographs and radiology reports for pulmonary edema assessment.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Joint modeling of chest radiographs and radiology reports for pulmonary edema assessment

Reference 5

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This paper cites Contrastive Localized Language-Image Pre-Training.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Contrastive Localized Language-Image Pre-Training

Reference 6

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This paper cites A review of medical image data augmentation techniques for deep learning applications.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting A review of medical image data augmentation techniques for deep learning applications

Reference 7

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This paper cites Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes

Reference 8

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This paper cites Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography

Reference 9

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This paper cites The Llama 3 Herd of Models.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting The Llama 3 Herd of Models

Reference 10

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This paper cites Devel- oping generalist foundation models from a multimodal dataset for 3d computed tomography.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Devel- oping generalist foundation models from a multimodal dataset for 3d computed tomography

Reference 11

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting LoRA: Low-Rank Adaptation of Large Language Models

Reference 12

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This paper cites Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition

Reference 13

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This paper cites Enhancing representation in medical vision-language foun- dation models via multi-scale information extraction techniques.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Enhancing representation in medical vision-language foun- dation models via multi-scale information extraction techniques

Reference 14

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This paper cites STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 15

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This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 16

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This paper cites Fool me twice: delayed diagnoses in radiology with emphasis on perpetuated errors.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Fool me twice: delayed diagnoses in radiology with emphasis on perpetuated errors

Reference 17

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This paper cites Generating synthetic data for medical imaging.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Generating synthetic data for medical imaging

Reference 18

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This paper cites Cxr-llava: a multimodal large language model for interpreting chest x-ray images.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Cxr-llava: a multimodal large language model for interpreting chest x-ray images

Reference 19

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This paper cites Llava-med: Training a large language-and-vision assistant for biomedicine in one day.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Llava-med: Training a large language-and-vision assistant for biomedicine in one day

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Artificial general intelligence for medical imaging analysis

Reference 21

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This paper cites Ct-glip: 3d grounded language-image pretraining with ct scans and radiology reports for full-body scenarios.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Ct-glip: 3d grounded language-image pretraining with ct scans and radiology reports for full-body scenarios

Reference 22

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Pmc-clip: Contrastive language-image pre-training using biomedical documents

Reference 23

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?

Reference 24

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Improved baselines with visual instruction tuning

Reference 25

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Representation Learning with Contrastive Predictive Coding

Reference 26

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Unsupervised medical image translation with adversarial diffusion models

Reference 27

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting On variational bounds of mutual information

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Learning transferable visual models from natural language supervision

Reference 29

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Study of thoracic ct in covid-19: the stoic project

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Deep learning in medical image analysis

Reference 31

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Large-scale and fine-grained vision- language pre-training for enhanced ct image understanding

Reference 32

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Bioclip: A vision foundation model for the tree of life

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models

Reference 34

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Communication errors in radiology–pitfalls and how to avoid them

Reference 35

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Multi- granularity cross-modal alignment for generalized medical visual representation learning

Reference 36

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Totalseg- mentator: robust segmentation of 104 anatomic structures in ct images

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Demystifying CLIP Data

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Glipv2: unifying local- ization and vl understanding

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks

Reference 42

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This paper cites RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis

Reference 43

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Development of a large-scale medical visual question-answering dataset

Reference 44

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Each of these claims is supported by theoretical analysis, ablation studies, and experimental results

Reference 45

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This paper cites Limitations.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Limitations

Reference 46

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This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 47

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MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that the paper does not include experiments

Reference 48

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This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 49

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Observation 81e41ff1-e8e3-43ed-9355-b8b9868615ca · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that the paper does not include experiments

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:12.017340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e1fad113-1c30-4f52-88ea-f48e99eed218 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that the paper does not include experiments

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:11.928213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 60d98338-22c4-40ca-957a-87b29c194562 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that the paper does not include experiments

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:11.810015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe82fdc4-fa2e-4d71-bcbe-0c308e46cdd5 · outbound

This paper cites All data used in this study are from publicly available, de- identified medical datasets, and no personally identifiable information (PII) was accessed or used.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting All data used in this study are from publicly available, de- identified medical datasets, and no personally identifiable information (PII) was accessed or used

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:11.712125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4cbe450f-4293-472e-80de-8a150b92989b · outbound

This paper cites Guidelines: 17 • The answer NA means that there is no societal impact of the work performed.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: 17 • The answer NA means that there is no societal impact of the work performed

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:11.553592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91091c49-eb63-4fbe-b291-6edfceb106fd · outbound

This paper cites an unresolved cited work.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:46:11.419872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 68c2e891-feb6-46ed-b4b6-2720a5c2d289 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Guidelines: • The answer NA means that the paper does not use existing assets

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:11.269223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 065dba93-e8cc-466b-ba0b-2abbd75001ec · outbound

This paper cites These assets will be released with accompanying documentation upon paper acceptance.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting These assets will be released with accompanying documentation upon paper acceptance

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:11.105435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5cf22d1e-5be8-4e8f-9c0d-983ca3baaf39 · outbound

This paper cites All data used are from publicly available, de-identified medical datasets with appropriate licenses and do not involve any direct interaction with individuals.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting All data used are from publicly available, de-identified medical datasets with appropriate licenses and do not involve any direct interaction with individuals

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:10.989629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 304aea77-f964-4354-8c2a-855d2a6a01d3 · outbound

This paper cites Therefore, IRB approval was not required.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Therefore, IRB approval was not required

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:10.820337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2905636d-5717-4d58-9d1d-4f6aeb97aac9 · outbound

This paper cites Answer: [Yes] Justification: Large language models such as GPT-4o and Qwen2.5, were used to rewrite radiology reports for improving semantic clarity during pretraining.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting Answer: [Yes] Justification: Large language models such as GPT-4o and Qwen2.5, were used to rewrite radiology reports for improving semantic clarity during pretraining

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:10.677975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers