Paper Citation Record · LEDGER

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2505.19779.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.19779 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:09:18.328973Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:09:14.550456Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T14:09:18.667383Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 227ee777-8b6e-4b7b-88f4-66e1bad503a8 · outbound

This paper cites In recent years, foundation models have gained significant interest in the research community, becoming a cornerstone in various fields of artificial intelligence [1].

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models In recent years, foundation models have gained significant interest in the research community, becoming a cornerstone in various fields of artificial intelligence [1]

Reference 1

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Observation 8fab2626-76f9-4ccf-a976-29fcdf4196b5 · outbound

This paper cites an unresolved cited work.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Unresolved cited work

Reference 2

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Observation e01d28ed-915d-4dfc-920e-6c79d37199ae · outbound

This paper cites an unresolved cited work.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Unresolved cited work

Reference 3

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Observation 147ba589-435f-48f7-9718-b4f6cd621a89 · outbound

This paper cites Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models

Reference 4

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Observation d6ed9bfb-df4f-45d0-a4b6-2230bb2f9570 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Masked autoencoders are scalable vision learners,

Reference 5

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Observation 5e5adc5f-1e7e-4a87-9f17-68e4f032cab0 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:15.711405Z

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Observation 62e5fde1-4eec-4c06-99a4-f44eb6619c20 · outbound

This paper cites For DDSM and CheXpert, the image channels were set to 1 since they are grayscale images.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models For DDSM and CheXpert, the image channels were set to 1 since they are grayscale images

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:09:21.513031Z

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Observation ef94b68e-75a4-4b66-8de8-556a26debe65 · outbound

This paper cites By fine-tuning these models on diverse datasets such as CBIS-DDSM, ISIC2019, APTOS2019, and CHEXPERT, we have demonstrated their capabil- ity to improve classification performance.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models By fine-tuning these models on diverse datasets such as CBIS-DDSM, ISIC2019, APTOS2019, and CHEXPERT, we have demonstrated their capabil- ity to improve classification performance

Reference 8

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Observation ed425630-bc3a-4b9c-b2b6-4e77a6c58580 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models On the Opportunities and Risks of Foundation Models

Reference 9

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Observation 29401fca-61dc-4916-a2ae-7259dddb1012 · outbound

This paper cites These computer vision foundation models exhibit robust performance, even in scenarios involving limited labeled data, by leveraging pre-trained knowledge.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models These computer vision foundation models exhibit robust performance, even in scenarios involving limited labeled data, by leveraging pre-trained knowledge

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:09:22.199305Z

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Observation 9581f044-f5ab-40e5-b864-8bd6e5f9259d · outbound

This paper cites Language models are unsupervised multitask learners,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Language models are unsupervised multitask learners,

Reference 11

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Observation 507b61f7-6888-4f49-903e-94ee0dbe6af6 · outbound

This paper cites We use the ViT-B and ViT-L models as the backbone for feature extraction.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models We use the ViT-B and ViT-L models as the backbone for feature extraction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:09:21.978446Z

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Observation 5e664320-faf9-4d89-a145-46b377b5169c · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understand- ing,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Bert: Pre-training of deep bidirectional transformers for language understand- ing,

Reference 13

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Observation d32f3552-3406-4a4e-ada3-1e4ea03fb7cb · outbound

This paper cites Learning transferable visual models from natural language su- pervision,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Learning transferable visual models from natural language su- pervision,

Reference 14

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Observation accf792a-f598-4856-a969-cd6d6d6ef850 · outbound

This paper cites VMamba: Visual State Space Model.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models VMamba: Visual State Space Model

Reference 15

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Observation a0f7bdec-420b-4794-b783-4a161a9c7380 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models SAM 2: Segment Anything in Images and Videos

Reference 16

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Observation 0728f44c-cc8d-4784-b3e7-b2147815bf8c · outbound

This paper cites Multimodal Autoregressive Pre-training of Large Vision Encoders.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Multimodal Autoregressive Pre-training of Large Vision Encoders

Reference 17

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Observation bd6b71ea-268a-4fd6-a5a5-3732dd53d4a3 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 18

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Observation 82745dbc-868d-4bfc-97c6-4da6e38b328b · outbound

This paper cites Pretrained ViTs Yield Versatile Representations For Medical Images.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Pretrained ViTs Yield Versatile Representations For Medical Images

Reference 19

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Observation 9a980b92-a5ae-40f4-b2ea-6d5f49bd65f5 · outbound

This paper cites What makes transfer learning work for medical im- ages: Feature reuse & other factors,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models What makes transfer learning work for medical im- ages: Feature reuse & other factors,

Reference 20

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Observation 3a117059-9ff3-4215-8d97-df7469e63293 · outbound

This paper cites Are natural domain foundation models useful for medical image classification?.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Are natural domain foundation models useful for medical image classification?

Reference 21

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Observation 3b80b03b-6836-45af-8f6e-ee11bda6214f · outbound

This paper cites Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation,

Reference 22

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Observation 2adcce12-53b0-4766-8b63-22c875c78ae5 · outbound

This paper cites MedCLIP: Contrastive Learning from Unpaired Medical Images and Text.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Reference 23

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Observation e5916206-f4b3-46a0-a8ca-057fa5cad46d · outbound

This paper cites Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection

Reference 24

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Observation 6871dd89-d511-4afa-9ee9-09b60ac0f510 · outbound

This paper cites Towards General Purpose Medical AI: Continual Learning Medical Foundation Model.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Towards General Purpose Medical AI: Continual Learning Medical Foundation Model

Reference 25

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This paper cites Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model

Reference 26

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Observation 8b3b63df-f6a6-4ea6-890e-800df09029ba · outbound

This paper cites Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey

Reference 27

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 28

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Observation 08073b84-b201-4dbe-828c-e327ba6311f3 · outbound

This paper cites Microsoft coco: Common objects in context,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Microsoft coco: Common objects in context,

Reference 29

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Observation 2e640bb5-06cd-44b4-8b8d-7b47852dd18d · outbound

This paper cites A curated mammography data set for use in computer-aided detection and diagnosis research,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models A curated mammography data set for use in computer-aided detection and diagnosis research,

Reference 30

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This paper cites Aptos 2019 blindness detection,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Aptos 2019 blindness detection,

Reference 31

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Observation c64e6b80-220a-48d0-8cf1-6137e74f554e · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Imagenet: A large-scale hierarchical image database,

Reference 32

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Observation 55901932-7aed-4280-ba37-3d6400372df5 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 33

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Observation e1b859ac-a82d-4aad-a1ab-a979dd4327ba · outbound

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Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Unresolved cited work

Reference 34

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Observation 677f8212-6b84-4301-a7e1-b9ac3b60a755 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:09:19.239779Z

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Observation 8472c7ff-dcb8-422e-8800-c5640af90fad · outbound

This paper cites The digital database for screening mammogra- phy,.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models The digital database for screening mammogra- phy,

Reference 36

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Pith citing papers

Observation 147ba589-435f-48f7-9718-b4f6cd621a89 · inbound