Paper Citation Record · LEDGER

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.19858.

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

pith.paper-citation-record.v1
2507.19858 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:03:58.861188Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 690b344a-4d84-45c7-8ff2-ddd4161b0e27 · outbound

This paper cites Deep learning for the harmonization of structural mri scans: a survey.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Deep learning for the harmonization of structural mri scans: a survey

Reference 1

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Observation 6413bcb6-6f3f-4f65-a012-a5d5af306f81 · outbound

This paper cites Multi-modal medical image classi- fication using deep residual network and genetic algorithm.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Multi-modal medical image classi- fication using deep residual network and genetic algorithm

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:01.055492Z

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Observation 8a7f82a1-689e-42d4-9d61-3c4ebbad3c4f · outbound

This paper cites Invariant risk minimization, 2020.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Invariant risk minimization, 2020

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:01.045790Z

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Observation 56b8b246-bc94-478e-b9ea-f7b9c47e9587 · outbound

This paper cites Multistage transfer learning for medical images.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Multistage transfer learning for medical images

Reference 4

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Observation fe14f626-6971-4bd9-99d4-c711bcc852fd · outbound

This paper cites Adaptive distribution learning with statistical hypothesis testing for covid-19 ct scan classification.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Adaptive distribution learning with statistical hypothesis testing for covid-19 ct scan classification

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:01.026497Z

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Observation 7dbe8bf1-1cc1-4d76-9a24-15c723505601 · outbound

This paper cites Advancing medical imaging informatics by deep learning-based domain adaptation.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Advancing medical imaging informatics by deep learning-based domain adaptation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:01.015755Z

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Observation 936fa082-1dd6-4d9d-9a8f-120dc8def44d · outbound

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

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation 761a2902-a0f1-42d4-814b-8f0e3f14a111 · outbound

This paper cites Long short-term memory.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Long short-term memory

Reference 8

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Observation 3f12800c-c755-4cc4-8ccc-703071915bb6 · outbound

This paper cites Domain-adversarial train- ing of neural networks, 2016.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Domain-adversarial train- ing of neural networks, 2016

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.995349Z

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Observation ce879644-972d-44a7-aaf0-fd7a7970fb84 · outbound

This paper cites Domain adaptive video segmentation via temporal consis- tency regularization.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Domain adaptive video segmentation via temporal consis- tency regularization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.985821Z

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Observation 153a6126-a862-46b6-afae-4dd09fbbd48e · outbound

This paper cites Domainatm: Domain adapta- tion toolbox for medical data analysis, 2022.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Domainatm: Domain adapta- tion toolbox for medical data analysis, 2022

Reference 11

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Observation 4a8a69f4-c197-46af-9e9d-c6c8f1d7af4b · outbound

This paper cites Deep residual learning for image recognition.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Deep residual learning for image recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.966287Z

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Observation de21da92-72e0-46ac-8d01-06cbc84aa928 · outbound

This paper cites Cmc-cov19d: Contrastive mixup classification for covid-19 diagnosis.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Cmc-cov19d: Contrastive mixup classification for covid-19 diagnosis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.956588Z

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Observation c214d450-8034-4cbd-830e-d8535a62de26 · outbound

This paper cites Cmc v2: Towards more ac- curate covid-19 detection with discriminative video priors,.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Cmc v2: Towards more ac- curate covid-19 detection with discriminative video priors,

Reference 14

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Observation d31d0a79-186b-49b7-873d-d908376cc1ce · outbound

This paper cites Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans

Reference 15

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Observation b62bbcca-4e13-41ad-b43f-ba037511d4d6 · outbound

This paper cites Bag of tricks of hybrid network for covid-19 detection of ct scans.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Bag of tricks of hybrid network for covid-19 detection of ct scans

Reference 16

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Observation ad50210b-4da0-4852-81a1-e05f6a2cfaee · outbound

This paper cites A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection

Reference 17

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Observation deeec33b-fa6b-4e2b-8ee4-5da80e27c4e0 · outbound

This paper cites Drct: Saving image super-resolution away from information bottleneck.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Drct: Saving image super-resolution away from information bottleneck

Reference 18

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Observation ecb97982-7600-415c-8dd9-e25f49194ae2 · outbound

This paper cites Revisiting vision- language features adaptation and inconsistency for so- cial media popularity prediction.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Revisiting vision- language features adaptation and inconsistency for so- cial media popularity prediction

Reference 19

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Observation 0d9bc586-fad9-40c1-94ee-95a022c15264 · outbound

This paper cites A lifelong learning approach to brain mr segmentation across scanners and protocols, 2018.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization A lifelong learning approach to brain mr segmentation across scanners and protocols, 2018

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.901004Z

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Observation b82def49-ab82-4ccb-abb8-7b862c3c84e5 · outbound

This paper cites Deep Transparent Prediction through Latent Representation Analysis.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Deep Transparent Prediction through Latent Representation Analysis

Reference 21

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Observation 3dfa331c-6e6e-4d73-be5b-a84c1fbffe47 · outbound

This paper cites Transpar- ent adaptation in deep medical image diagnosis.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Transpar- ent adaptation in deep medical image diagnosis

Reference 22

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Observation add02daf-6aac-439b-9140-c9ee951f6f6b · outbound

This paper cites Mia-cov19d: Covid-19 detection through 3-d chest ct image analysis.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Mia-cov19d: Covid-19 detection through 3-d chest ct image analysis

Reference 23

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Observation cf6e3b77-663d-4fc1-83a4-a5daf14e9456 · outbound

This paper cites Ai-mia: Covid-19 detection and severity analysis through medical imaging.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Ai-mia: Covid-19 detection and severity analysis through medical imaging

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.872612Z

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Observation 0d5ed0a6-8a2b-49bb-b4ba-8413795d6392 · outbound

This paper cites A deep neural architecture for harmonizing 3-d input data analysis and decision making in medical imaging.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization A deep neural architecture for harmonizing 3-d input data analysis and decision making in medical imaging

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.863028Z

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Observation 96537e55-2cbe-426f-af51-055340642a29 · outbound

This paper cites Domain adaptation explainability & fairness in ai for medical image analysis: Diagnosis of covid-19 based on 3-d chest ct-scans.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Domain adaptation explainability & fairness in ai for medical image analysis: Diagnosis of covid-19 based on 3-d chest ct-scans

Reference 26

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Observation f9c10298-ac40-4ca6-8d3b-50cf4d630244 · outbound

This paper cites SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection

Reference 27

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Observation 5e7a909e-68ff-4a0f-9c14-6865189d828e · outbound

This paper cites Out-of-distribution generaliza- tion via risk extrapolation (rex), 2021.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Out-of-distribution generaliza- tion via risk extrapolation (rex), 2021

Reference 28

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Observation fc3dee2c-b65d-4d59-89fa-5cfb7ff8fa3e · outbound

This paper cites Re- thinking domain adaptation for machine learning over clini- cal language.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Re- thinking domain adaptation for machine learning over clini- cal language

Reference 29

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Observation 8949ffb5-ab75-42ba-87f7-0c6bb0b571d5 · outbound

This paper cites Prompthsi: Universal hyperspectral image restoration with vision-language modulated frequency adaptation, 2025.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Prompthsi: Universal hyperspectral image restoration with vision-language modulated frequency adaptation, 2025

Reference 30

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Observation e389d468-1a96-46fa-83e6-7206d8ff79f2 · outbound

This paper cites Enhancing and adapt- ing in the clinic: Source-free unsupervised domain adapta- tion for medical image enhancement.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Enhancing and adapt- ing in the clinic: Source-free unsupervised domain adapta- tion for medical image enhancement

Reference 31

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Observation 655f8b24-4fdc-47ba-a021-a6fa19d0560c · outbound

This paper cites Brooks, Nathan Jacobs, and Jin Chen.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Brooks, Nathan Jacobs, and Jin Chen

Reference 32

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Observation 4f0c4071-ecd5-4f95-af66-37df64394314 · outbound

This paper cites Divide and conquer: Grounding a bleeding areas in gastrointestinal image with two-stage model, 2024.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Divide and conquer: Grounding a bleeding areas in gastrointestinal image with two-stage model, 2024

Reference 33

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Observation 6198132a-2f64-4e4c-9b21-83c254a8c3b7 · outbound

This paper cites Densesr: Image shadow removal as dense prediction, 2025.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Densesr: Image shadow removal as dense prediction, 2025

Reference 34

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Observation a54cb753-36a8-45c9-908b-7c686113fd1a · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 35

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Observation 6a57b38c-f33d-4d6b-9e86-bd9576935534 · outbound

This paper cites Stan-ct: Standardizing ct image using generative adversarial network, 2020.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Stan-ct: Standardizing ct image using generative adversarial network, 2020

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.751923Z

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Observation d8eb6bbd-5cad-434a-817a-ac8592e34675 · outbound

This paper cites Ct image harmonization for enhancing radiomics studies.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Ct image harmonization for enhancing radiomics studies

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.486625Z

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Observation 1b467a53-4968-47de-bc9b-ba50a72c62aa · outbound

This paper cites Cross-vendor ct image data harmonization using cvh-ct, 2021.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Cross-vendor ct image data harmonization using cvh-ct, 2021

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:04:00.260571Z

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Observation c72fa071-5278-4a8e-b056-39b26c4f1e6e · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:03:59.965725Z

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Observation ea419667-b6a0-4855-a872-d7fafa814fc6 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 40

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Source-reported events for the cited work

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Observation bb6e308c-5534-4572-b448-37ec32215b69 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Tent: Fully test-time adaptation by entropy minimization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:03:59.639769Z

Source-reported events for the cited work

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Observation 116fc9cc-d922-4fab-a94f-6ec5f36f97b1 · outbound

This paper cites Multi-source covid-19 de- tection via variance risk extrapolation, 2025.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Multi-source covid-19 de- tection via variance risk extrapolation, 2025

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:03:59.386562Z

Source-reported events for the cited work

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Observation ada7d397-13d7-4a89-853c-e13d537d2d4d · outbound

This paper cites an unresolved cited work.

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization Unresolved cited work

Reference 2021

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.