Paper Citation Record · LEDGER

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST

As of 17 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2501.14685.

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

pith.paper-citation-record.v1
2501.14685 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:56:56.292811Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T22:30:41.001352Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:34:01.486333Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e15b9634-1e8b-44f6-b195-cd411dfc0a1f · outbound

This paper cites On biases in a UK B iobank-based retinal image classification model.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST On biases in a UK B iobank-based retinal image classification model

Reference 1

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Observation c33cc538-2438-4233-b652-4eecf0aaaa96 · outbound

This paper cites PaLM 2 Technical Report.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST PaLM 2 Technical Report

Reference 2

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Observation d7ec7cb1-04a8-442c-a373-408d70b51103 · outbound

This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 3

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Observation 3eb4821b-7c4c-4dde-bd5e-4103a32c43da · outbound

This paper cites Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

Reference 4

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Observation 4097a3eb-e56d-4be1-a5f8-12523c529b99 · outbound

This paper cites Universeg: Universal medical image segmentation.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Universeg: Universal medical image segmentation

Reference 5

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Observation 1f15f8d3-ec22-4fb9-a233-7cf9ae8255e6 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Emerging properties in self-supervised vision transformers

Reference 6

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Observation 6095046a-b2bd-454d-a072-47d48a04c38f · outbound

This paper cites When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Reference 7

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Observation 725378fc-3082-48d2-aa1d-919681894adc · outbound

This paper cites A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

Reference 8

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Observation 00aec14f-59ee-4e56-8390-d3adf164b8ee · outbound

This paper cites Rethinking model prototyping through the MedMNIST+ dataset collection.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Rethinking model prototyping through the MedMNIST+ dataset collection

Reference 9

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Observation a1c0b57c-155a-4876-9614-b7eba9559fbc · outbound

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

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 626152c7-a7f6-420b-b21d-1dc031175d50 · outbound

This paper cites Eva-02: A visual representation for neon genesis.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Eva-02: A visual representation for neon genesis

Reference 11

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Observation 022fa2ea-1945-44d7-bff2-7d5f9b3889eb · outbound

This paper cites Deep residual learning for image recognition.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Deep residual learning for image recognition

Reference 12

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Observation bf2e7b03-278d-43d8-880e-ae4c7e1f1adf · outbound

This paper cites Densely connected convolutional networks.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Densely connected convolutional networks

Reference 13

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Observation 874f6eab-24e2-43d0-a332-6e4453ab3857 · outbound

This paper cites Audiogpt: Understanding and generating speech, music, sound, and talking head.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Audiogpt: Understanding and generating speech, music, sound, and talking head

Reference 14

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Observation 157cb0a7-9d5b-4170-a508-929f98f579fa · outbound

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Unresolved cited work

Reference 15

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Observation c9a4bbfb-fd60-427c-9f24-40812e67f970 · outbound

This paper cites Openclip, July 2021.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Openclip, July 2021

Reference 16

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Observation b2a6e98e-6a80-4f51-b17c-5509cbb775d5 · outbound

This paper cites Segment anything.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Segment anything

Reference 17

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Observation 8128c184-c0bc-4833-821c-c9218a5a4fd9 · outbound

This paper cites Decoupled Weight Decay Regularization.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Decoupled Weight Decay Regularization

Reference 18

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Observation 749dce5f-a2fb-4bd5-8a2a-a575eec970e8 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Sgdr: Stochastic gradient descent with warm restarts

Reference 19

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Observation c1c4600d-a98c-42c7-a367-2ea30f448def · outbound

This paper cites Segment anything in medical images.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Segment anything in medical images

Reference 20

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Observation ab93698d-16ce-4e17-8009-c9f594f0f02d · outbound

This paper cites Segment anything model for medical image analysis: an experimental study.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Segment anything model for medical image analysis: an experimental study

Reference 21

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Observation 41885b26-903a-4f19-9980-068620a052d4 · outbound

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

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST DINOv2: Learning Robust Visual Features without Supervision

Reference 22

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Observation 502ec422-db53-4a31-a497-07b3e5c1850f · outbound

This paper cites Imagenet large scale visual recognition challenge.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Imagenet large scale visual recognition challenge

Reference 23

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Observation f5bbc1be-6964-4cd8-ab89-0ce086e23baf · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 24

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Observation 62524510-696c-46e4-a805-c7f1c8a93602 · outbound

This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 25

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Observation f34beb2a-de65-4b6d-b9b0-82851a376858 · outbound

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 26

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Observation 653c1213-d9fa-4639-a548-43a0caad3764 · outbound

This paper cites Xraygpt: Chest radiographs summarization using large medical vision-language models.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Xraygpt: Chest radiographs summarization using large medical vision-language models

Reference 27

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Observation ec007d16-cd35-4598-b27f-85dbc1e5aea9 · outbound

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

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST LLaMA: Open and Efficient Foundation Language Models

Reference 28

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Observation a086083e-d436-4dc4-89c7-4ee4a16f6ec1 · outbound

This paper cites A real-world dataset and benchmark for foundation model adaptation in medical image classification.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST A real-world dataset and benchmark for foundation model adaptation in medical image classification

Reference 29

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Visionllm: Large language model is also an open-ended decoder for vision-centric tasks

Reference 30

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Pytorch image models

Reference 31

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Observation 8640048a-2f20-4b45-bb62-4e5eb3be00f0 · outbound

This paper cites Navigating data scarcity using foundation models: A benchmark of few-shot and zero-shot learning approaches in medical imaging.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Navigating data scarcity using foundation models: A benchmark of few-shot and zero-shot learning approaches in medical imaging

Reference 32

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This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 33

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST A generalist vision--language foundation model for diverse biomedical tasks

Reference 34

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Observation 8d0ce9d3-a456-4400-866a-a82cd16694b7 · outbound

This paper cites On the challenges and perspectives of foundation models for medical image analysis.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST On the challenges and perspectives of foundation models for medical image analysis

Reference 35

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 36

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Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Recommender systems in the era of large language models (llms)

Reference 37

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This paper cites A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Reference 38

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Pith citing papers

Observation c8c3eae2-0801-45fd-bdab-a03e8b5805ec · inbound

Benchmarking PNW Model for MedMNIST to 100% Accuracy cites this paper.

Benchmarking PNW Model for MedMNIST to 100% Accuracy Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:21:02.927043Z

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Observation 6e798033-088d-4f19-b536-5e3e035f8b25 · inbound

CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection cites this paper.

CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST

Reference 31

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