VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine | Paper Citation Record

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

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Gloria: A multimodal global- local representation learning framework for label-efficient medical image recognition

Reference 1

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Observation 8e0f9474-ea4f-4127-9d48-13630f15a833 · outbound

This paper cites Medclip: Contrastive learning from unpaired medical images and text.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Medclip: Contrastive learning from unpaired medical images and text

Reference 2

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This paper cites MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations

Reference 3

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Observation aeb99c0f-ea84-4b85-8a41-045a9e128482 · outbound

This paper cites Towards unifying medical vision-and-language pre-training via soft prompts.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Towards unifying medical vision-and-language pre-training via soft prompts

Reference 4

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Observation 0da28f04-e7b2-43ac-8fe4-e3b7a886af18 · outbound

This paper cites Learning transferable visual models from natural language supervision.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Learning transferable visual models from natural language supervision

Reference 5

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Observation 31fd3ddf-0019-47de-87cc-ccc666725f89 · outbound

This paper cites Conditional prompt learning for vision- language models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Conditional prompt learning for vision- language models

Reference 6

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Observation a82cf4d4-0d5e-44a8-a9cc-09e4c9d47e98 · outbound

This paper cites Learning to prompt for vision-language models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Learning to prompt for vision-language models

Reference 7

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Observation 3d24152b-dd7f-4b0f-b4a2-dc688ba3e347 · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Groupvit: Semantic segmentation emerges from text supervision

Reference 8

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Observation 31fd943f-85c2-41fd-80ff-adf890337742 · outbound

This paper cites Learning to exploit temporal structure for biomedical vision-language processing.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Learning to exploit temporal structure for biomedical vision-language processing

Reference 9

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Observation 1b345788-b913-4dd1-9761-227c17a82168 · outbound

This paper cites Cplip: zero-shot learning for histopathology with comprehensive vision-language alignment.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Cplip: zero-shot learning for histopathology with comprehensive vision-language alignment

Reference 10

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Observation 86beeb90-f59c-497a-bb3a-c9b04b201fe4 · outbound

This paper cites Lu, Bowen Chen, Andrew Zhang, Drew F.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Lu, Bowen Chen, Andrew Zhang, Drew F

Reference 11

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Observation 68253beb-c63f-4592-be58-8e38d3c53ec3 · outbound

This paper cites Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports

Reference 12

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Observation 75d4139c-ac4d-494b-8a81-927e794d3f02 · outbound

This paper cites Quilt-1M: One Million Image-Text Pairs for Histopathology.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Quilt-1M: One Million Image-Text Pairs for Histopathology

Reference 13

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This paper cites Towards generalist foundation model for radiology.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Towards generalist foundation model for radiology

Reference 14

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Observation 1c8df07b-4282-4af2-aa22-24f49dff84f6 · outbound

This paper cites M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

Reference 15

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Observation 49a9153b-bea6-4ccc-a6a9-ce080367e416 · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Align before fuse: Vision and language representation learning with momentum distillation

Reference 16

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This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 17

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This paper cites Momentum contrast for unsupervised visual representation learning.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Momentum contrast for unsupervised visual representation learning

Reference 18

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This paper cites A simple framework for contrastive learning of visual representations.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine A simple framework for contrastive learning of visual representations

Reference 19

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Observation 37c0d002-2bd2-46df-85b9-868fba55a5fc · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Bootstrap your own latent-a new approach to self-supervised learning

Reference 20

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Observation 5fca479a-bce1-4cd0-9d94-f14a8b21dab4 · outbound

This paper cites Masked autoencoders are scalable vision learners.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Masked autoencoders are scalable vision learners

Reference 21

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This paper cites Generative pretraining from pixels.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Generative pretraining from pixels

Reference 22

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Observation 06722dc4-a9ea-472f-beba-ce93f12056e7 · outbound

This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 23

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This paper cites Colorization as a proxy task for visual understanding.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Colorization as a proxy task for visual understanding

Reference 24

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This paper cites Self-supervised representation learning by rotation feature decoupling.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Self-supervised representation learning by rotation feature decoupling

Reference 25

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This paper cites What makes instance discrimination good for transfer learning?.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine What makes instance discrimination good for transfer learning?

Reference 26

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Observation d4d30880-b7a7-407a-a716-b7c9dd9545f4 · outbound

This paper cites Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning

Reference 27

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Observation d8a30164-549d-4257-b6ae-c5d0719a8c23 · outbound

This paper cites Attention is all you need.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Attention is all you need

Reference 28

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This paper cites Language models are few-shot learners.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Language models are few-shot learners

Reference 29

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine LLaMA: Open and Efficient Foundation Language Models

Reference 30

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This paper cites Med3D: Transfer Learning for 3D Medical Image Analysis.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Med3D: Transfer Learning for 3D Medical Image Analysis

Reference 31

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This paper cites Models genesis.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Models genesis

Reference 32

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Observation 724bb59d-0b2c-45c6-99ab-a656e72b9bd5 · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

Reference 33

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This paper cites LXMERT: Learning Cross-Modality Encoder Representations from Transformers.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Reference 34

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This paper cites Uniter: Universal image-text representation learning.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Uniter: Universal image-text representation learning

Reference 35

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Observation f6b30fb9-6e03-4c3a-add7-a7fcd66b4145 · outbound

This paper cites Oscar: Object-semantics aligned pre-training for vision-language tasks.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Oscar: Object-semantics aligned pre-training for vision-language tasks

Reference 36

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Observation a5ce9f45-656d-4090-a193-445a6a8a5b1b · outbound

This paper cites Vinvl: Revisiting visual representations in vision-language models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Vinvl: Revisiting visual representations in vision-language models

Reference 37

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This paper cites Coca: Contrastive captioners are image-text foundation models, 2022.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Coca: Contrastive captioners are image-text foundation models, 2022

Reference 38

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 39

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 40

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Observation 6df0aa4f-a65e-41d4-a663-6f98aff101bf · outbound

This paper cites Multi-modal understanding and generation for medical images and text via vision-language pre-training.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Multi-modal understanding and generation for medical images and text via vision-language pre-training

Reference 41

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Observation ff93f537-8c21-40d3-a176-520255c67df5 · outbound

This paper cites PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

Reference 42

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This paper cites Slip: Self-supervision meets language- image pre-training.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Slip: Self-supervision meets language- image pre-training

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.678960Z

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Observation 30c66838-3b1e-4226-9be9-6e0896c0c7c8 · outbound

This paper cites Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm, 2022.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm, 2022

Reference 44

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Observation e0504519-2856-4003-a9bc-3a893792e187 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Swin transformer: Hierarchical vision transformer using shifted windows

Reference 45

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Observation 35a0962c-c2a3-47dc-a28a-8495e21b17fd · outbound

This paper cites Self-supervised pre-training of swin transformers for 3d medical image analysis.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Self-supervised pre-training of swin transformers for 3d medical image analysis

Reference 46

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Observation d36c25de-c9bd-462e-9e61-ddc1f30d4e7f · outbound

This paper cites Masked image modeling advances 3d medical image analysis, 2022.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Masked image modeling advances 3d medical image analysis, 2022

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.633279Z

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Observation a1337afe-146a-4800-bfa3-7a63abe36566 · outbound

This paper cites V oco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine V oco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.619401Z

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Observation 39c3030c-6260-4ec2-bd81-ceaee3ac0f57 · outbound

This paper cites Context Encoders: Feature Learning by Inpainting.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Context Encoders: Feature Learning by Inpainting

Reference 49

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This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Unsupervised Representation Learning by Predicting Image Rotations

Reference 50

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Observation 18bc6f70-c1eb-4cf3-8c96-c2732c5712ac · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Representation Learning with Contrastive Predictive Coding

Reference 51

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Observation 0f456fc9-a844-4b98-8b47-c3c06dd978f3 · outbound

This paper cites Fast WordPiece Tokenization.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Fast WordPiece Tokenization

Reference 52

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Observation e9a74340-58c9-4891-88f7-ce9118f5469c · outbound

This paper cites Zero-shot text-to-image generation.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Zero-shot text-to-image generation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.604840Z

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Observation 35db7e1a-a920-464b-accd-d190d1e28d70 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:01.138329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5231ee45-4b53-4ccf-bf1c-777711b32a05 · outbound

This paper cites Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6695–6714, 2022.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6695–6714, 2022

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.581111Z

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 ee2307a5-8a02-497e-bdf9-dbeedff77eff · outbound

This paper cites Ct-org, a new dataset for multiple organ segmentation in computed tomography.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Ct-org, a new dataset for multiple organ segmentation in computed tomography

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.566777Z

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 517d1b0f-fc32-4797-9814-9daa32543141 · outbound

This paper cites Ledsam, and Olaf Ronneberger.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Ledsam, and Olaf Ronneberger

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.553169Z

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 0419b32a-6f0d-4f36-92ed-505400bf3365 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Bleu: a method for automatic evaluation of machine translation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.539265Z

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 6787a5d8-402d-4bd3-92a5-6ae847698a9b · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Rouge: A package for automatic evaluation of summaries

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.524066Z

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 759c3292-5139-4c5c-b49f-4d11bdce6f35 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine BERTScore: Evaluating Text Generation with BERT

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:01.166429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b728e4d2-5e7e-44e3-bf75-bf7ca624c657 · outbound

This paper cites Decoupled weight decay regularization, 2019.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Decoupled weight decay regularization, 2019

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:01.170711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77ac4f29-0498-4704-82b8-6e3cb1cf4238 · outbound

This paper cites https://huggingface.co/ContactDoctor/Bio- Medical-Llama-3-8B, 2024.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine https://huggingface.co/ContactDoctor/Bio- Medical-Llama-3-8B, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.500789Z

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 0e4abb36-f73f-4b20-98c6-4184023dd95c · outbound

This paper cites Lora: Low-rank adaptation of large language models.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Lora: Low-rank adaptation of large language models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:01.179662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff94ef9a-2544-4cf9-8091-f84bc85c13bd · outbound

This paper cites Segment anything.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Segment anything

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:01.184855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 807c9353-43ba-4bfa-8ccb-a1f472aea0fc · outbound

This paper cites Segvol: Universal and interactive volumetric medical image segmentation.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Segvol: Universal and interactive volumetric medical image segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.467821Z

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 70085536-0c4e-4680-b283-50c93e0ab6bd · outbound

This paper cites Segment anything in medical images.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Segment anything in medical images

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.453474Z

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 d7497ca5-54bc-45f8-9024-6fce54bf1fea · outbound

This paper cites Pmc- clip: Contrastive language-image pre-training using biomedical documents.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Pmc- clip: Contrastive language-image pre-training using biomedical documents

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:01.438351Z

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 f449df5a-836c-4ed5-8d9d-055db0c5d757 · outbound

This paper cites Accelerate: Training and inference at scale made simple, efficient and adaptable.

VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine Accelerate: Training and inference at scale made simple, efficient and adaptable

Reference 68

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T17:30:01.424454Z

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