Paper Citation Record · LEDGER

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2502.05517.

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

pith.paper-citation-record.v1
2502.05517 v2

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measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-05-22T15:31:44.933646Z

Reference resolution

42 of 42 outbound references displayed

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Outbound references

Observation 7b455eb2-6d94-477c-be5a-69570525b2d8 · outbound

This paper cites Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Reference 1

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This paper cites Brain tumor classification using deep cnn features via transfer learning.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Brain tumor classification using deep cnn features via transfer learning

Reference 2

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This paper cites Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Reference 3

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Observation aed47d57-a7bb-4e4b-a542-cd95c92b5ec1 · outbound

This paper cites Deep learning–based histopatho- logic assessment of kidney tissue.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Deep learning–based histopatho- logic assessment of kidney tissue

Reference 4

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Observation 48f82def-39b8-44e4-87b6-79af5800cbb7 · outbound

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

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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Observation 5626abf8-4c37-441a-994f-fd61b6c0b67a · outbound

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

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Swin transformer: Hierarchical vision transformer using shifted windows

Reference 6

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This paper cites Swin transformer v2: Scaling up capacity and resolution.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Swin transformer v2: Scaling up capacity and resolution

Reference 7

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This paper cites Maxvit: Multi-axis vision transformer.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Maxvit: Multi-axis vision transformer

Reference 8

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This paper cites Multi cancer dataset [dataset], 2022.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Multi cancer dataset [dataset], 2022

Reference 9

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This paper cites The IQ-OTH/NCCD lung cancer dataset.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors The IQ-OTH/NCCD lung cancer dataset

Reference 10

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This paper cites The IQ-OTH/NCCD lung cancer dataset.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors The IQ-OTH/NCCD lung cancer dataset

Reference 11

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This paper cites CT KIDNEY DATASET: Normal-cyst-tumor and stone.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors CT KIDNEY DATASET: Normal-cyst-tumor and stone

Reference 12

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This paper cites High accuracy brain tumor classification with EfficientNet and magnetic resonance images.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors High accuracy brain tumor classification with EfficientNet and magnetic resonance images

Reference 13

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Observation 6fc65a44-e1f3-43f3-8a5a-af545fb7b8ed · outbound

This paper cites Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging

Reference 14

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This paper cites A recent survey of vision transformers for medical image segmentation.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors A recent survey of vision transformers for medical image segmentation

Reference 15

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This paper cites Enhanced performance of brain tumor classification via tumor region augmentation and partition.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Enhanced performance of brain tumor classification via tumor region augmentation and partition

Reference 16

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This paper cites A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification

Reference 17

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This paper cites Brain tumor classification via statistical features and back-propagation neural network.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Brain tumor classification via statistical features and back-propagation neural network

Reference 18

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Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Comparative approach of mri-based brain tumor segmentation and classification using genetic algorithm

Reference 19

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This paper cites Brain tumor type classification via capsule networks.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Brain tumor type classification via capsule networks

Reference 20

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This paper cites Multi- grade brain tumor classification using deep cnn with extensive data augmentation.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Multi- grade brain tumor classification using deep cnn with extensive data augmentation

Reference 21

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This paper cites Deep cnn for brain tumor classification.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Deep cnn for brain tumor classification

Reference 22

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This paper cites Deep transfer learning approaches in performance analysis of brain tumor classification using mri images.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Deep transfer learning approaches in performance analysis of brain tumor classification using mri images

Reference 23

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Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Unresolved cited work

Reference 24

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This paper cites Detection of lung cancer using svm classifier and knn algorithm.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Detection of lung cancer using svm classifier and knn algorithm

Reference 25

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Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Multi-scale convolutional neural networks for lung nodule classification

Reference 26

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This paper cites Multi-view multi-scale cnns for lung nodule type classification from ct images.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Multi-view multi-scale cnns for lung nodule type classification from ct images

Reference 27

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Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Ensemble learners of multiple deep cnns for pulmonary nodules classification using ct images

Reference 28

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Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Bolan, Michael B

Reference 29

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This paper cites Towards reliable and explainable ai model for pulmonary nodule diagnosis.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Towards reliable and explainable ai model for pulmonary nodule diagnosis

Reference 30

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This paper cites Kim and J.-W.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Kim and J.-W

Reference 31

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This paper cites Atlas-based semi-automatic kidney tumor detection and segmentation in ct images.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Atlas-based semi-automatic kidney tumor detection and segmentation in ct images

Reference 32

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This paper cites Machine learning-based quantitative texture analysis of ct images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Machine learning-based quantitative texture analysis of ct images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma

Reference 33

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This paper cites Prediction of benign and malignant solid renal masses: machine learning-based ct texture analysis.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Prediction of benign and malignant solid renal masses: machine learning-based ct texture analysis

Reference 34

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This paper cites Kidney tumor detection using attention based U-Net.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Kidney tumor detection using attention based U-Net

Reference 35

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Observation 80e4c82a-ba05-4d76-a715-a88b2801a738 · outbound

This paper cites Automated segmentation of kidney and renal mass and automated detection of renal mass in ct urography using 3d u-net-based deep convolutional neural network.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Automated segmentation of kidney and renal mass and automated detection of renal mass in ct urography using 3d u-net-based deep convolutional neural network

Reference 36

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This paper cites Kidney tumor detection and classification based on deep learning approaches: a new dataset in ct scans.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Kidney tumor detection and classification based on deep learning approaches: a new dataset in ct scans

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:05:02.930556Z

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This paper cites Resnet and resnext-powered kidney tumor detection: A robust approach on a subset of the kauh dataset.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Resnet and resnext-powered kidney tumor detection: A robust approach on a subset of the kauh dataset

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:05:02.911424Z

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This paper cites Kidney tumor classification on ct images using self-supervised learning.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Kidney tumor classification on ct images using self-supervised learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:05:02.894040Z

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This paper cites Development and testing of an led-based near-infrared sensor for human kidney tumor diagnostics.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Development and testing of an led-based near-infrared sensor for human kidney tumor diagnostics

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:05:02.877117Z

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This paper cites Brain tumor dataset, 4 2017.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Brain tumor dataset, 4 2017

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:05:02.860618Z

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Observation 219fb593-4f6f-442b-b796-421422c6d145 · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors Efficientnetv2: Smaller models and faster training

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:05:02.842993Z

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Pith citing papers

Observation dff5b39f-3d8c-4a28-bbfc-5dadfdc22c21 · inbound

Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis cites this paper.

Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors

Reference 8

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