Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution | Paper Citation Record

Observation b942e952-eec1-4c57-bed8-05986d2fe512 · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 1

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.858963Z

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Observation a4031129-0770-4b18-b0ca-89012252a429 · outbound

This paper cites Low-field MRI: Clinical promise and challenges.Journal of Magnetic Resonance Imaging, 57(1): 25–44.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Low-field MRI: Clinical promise and challenges.Journal of Magnetic Resonance Imaging, 57(1): 25–44

Reference 2

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.865289Z

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Observation 1447c6a1-3ff7-46aa-b534-067afaef8b9b · outbound

This paper cites A framework for advancing sustainable magnetic resonance imaging access in Africa.NMR in Biomedicine, 36(3):e4846.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution A framework for advancing sustainable magnetic resonance imaging access in Africa.NMR in Biomedicine, 36(3):e4846

Reference 3

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.862214Z

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Observation 9b998744-97e2-45a0-b7f5-1eb63dd94439 · outbound

This paper cites Low field, high impact: Democratizing MRI for clinical and research innovation.BJR |Open, 7(1):tzaf022.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Low field, high impact: Democratizing MRI for clinical and research innovation.BJR |Open, 7(1):tzaf022

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-05-17T19:10:07.984227Z

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Observation c298044f-d3b0-4575-9288-2640ce962cea · outbound

This paper cites Temporal and spatial super resolution with latent diffusion model in medical MRI images.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Temporal and spatial super resolution with latent diffusion model in medical MRI images

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:07.990652Z

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Observation 981c5331-d96b-4525-83ae-f5508f56d5bf · outbound

This paper cites InverseSR: 3D brain MRI super-resolution using a latent diffusion model.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution InverseSR: 3D brain MRI super-resolution using a latent diffusion model

Reference 6

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.852545Z

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Observation e060be5a-d635-4bd8-a414-981de62237ab · outbound

This paper cites sFRC for assessing hallucinations in medical image restoration.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution sFRC for assessing hallucinations in medical image restoration

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:07.987454Z

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Observation f98c56aa-1640-4db8-b00f-d6e9feea54de · outbound

This paper cites Hallucination score: Towards mitigating hallucinations in generative image super-resolution.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Hallucination score: Towards mitigating hallucinations in generative image super-resolution

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:07.981113Z

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Observation 5bba14f3-3980-43c4-acb4-53e7d512daef · outbound

This paper cites Jones, Jonathan Lee, and Meng Law.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Jones, Jonathan Lee, and Meng Law

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:07.977849Z

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Observation 9b56d6db-be20-4501-8fbb-17bd5044b8b0 · outbound

This paper cites doi: 10.1016/j.mric.2020.09.001.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution doi: 10.1016/j.mric.2020.09.001

Reference 10

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.841038Z

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Observation f114eae9-c379-47ad-9c8b-cffae20689be · outbound

This paper cites Ladd, Peter Bachert, Martin Meyerspeer, Ewald Moser, Armin M.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Ladd, Peter Bachert, Martin Meyerspeer, Ewald Moser, Armin M

Reference 11

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.844401Z

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Observation d5f88576-ef1d-44c4-bb6c-0137986ec978 · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T15:45:33.848769Z

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Observation 7471c581-d6c5-47b4-99af-3c29186adbfe · outbound

This paper cites Image super-resolution: The techniques, applications, and future.Signal Processing, 128:389–408, November 2016.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Image super-resolution: The techniques, applications, and future.Signal Processing, 128:389–408, November 2016

Reference 13

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.798501Z

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Observation 57b686a1-334e-41c4-845f-e5799049c0f3 · outbound

This paper cites A deep journey into super-resolution: A survey.ACM Computing Surveys, 53(3):60:1–60:34.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution A deep journey into super-resolution: A survey.ACM Computing Surveys, 53(3):60:1–60:34

Reference 14

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.775634Z

Source-reported events for the cited work

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Observation b0bcde80-32b8-4a9c-810b-2bc5e529f2ac · outbound

This paper cites Du, Dennis L.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Du, Dennis L

Reference 15

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.801278Z

Source-reported events for the cited work

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Observation 882af65e-01b2-4922-85b6-02012a3790a1 · outbound

This paper cites Greenspan, G.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Greenspan, G

Reference 16

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.764513Z

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Observation 2c17dde7-3742-413c-9f5b-cb9c47aea88f · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 17

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.758796Z

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Observation 8c7af89d-80fb-48be-ae07-4becd372de24 · outbound

This paper cites Multiscale brain MRI super-resolution using deep 3D convolutional networks.Computerized Medical Imaging and Graphics, 77:101647, October 2019.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Multiscale brain MRI super-resolution using deep 3D convolutional networks.Computerized Medical Imaging and Graphics, 77:101647, October 2019

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-10T15:45:33.813380Z

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Observation f82b0a9e-5ee9-48b8-a937-08dcab9d13b1 · outbound

This paper cites Real-time automatic fetal brain extraction in fetal MRI by deep learning.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Real-time automatic fetal brain extraction in fetal MRI by deep learning

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:45:33.817474Z

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Observation 4f9f6459-33f3-4d64-b587-c22684812c77 · outbound

This paper cites Masutani, Naeim Bahrami, and Albert Hsiao.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Masutani, Naeim Bahrami, and Albert Hsiao

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.023370Z

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Observation 944540c5-2812-4330-a6dd-48ee35339e81 · outbound

This paper cites doi: 10.1148/radiol.2020192173.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution doi: 10.1148/radiol.2020192173

Reference 21

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.822445Z

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Observation 9738c439-84a5-4107-b9a5-f533c4d8bd38 · outbound

This paper cites Christodoulou, Yibin Xie, Zhengwei Zhou, and Debiao Li.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Christodoulou, Yibin Xie, Zhengwei Zhou, and Debiao Li

Reference 22

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.820007Z

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Observation ddb05eef-405f-4ef8-a569-52cdda161922 · outbound

This paper cites ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, September 2018.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, September 2018

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.013638Z

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Observation 708f123b-8ea5-4582-b54a-dc7ca70be154 · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 24

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.803797Z

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Observation 3cbf24c3-1add-440d-9755-294586fb0a20 · outbound

This paper cites Martin, Rhodri Cusack, and Stefan Köhler.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Martin, Rhodri Cusack, and Stefan Köhler

Reference 25

Resolution: malformed identifier; doi_truncated, observed 2026-05-10T15:45:33.795325Z

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Observation f23d66dd-9b1e-4bc0-af44-6e2584e072c0 · outbound

This paper cites Kiki- net: Cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Kiki- net: Cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images

Reference 26

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.806558Z

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Observation 65fb74f6-3a5f-41d8-91cc-6dcecfc51723 · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 27

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.785483Z

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Observation 9a0425d4-75bf-4b07-ad14-e810c7eb2b46 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution High-resolution image synthesis with latent diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.017071Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80dc6c0b-5785-4b59-bae4-6f6321d852da · outbound

This paper cites Medical image super-resolution reconstruc- tion algorithms based on deep learning: A survey.Comput.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Medical image super-resolution reconstruc- tion algorithms based on deep learning: A survey.Comput

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-10T15:45:33.792674Z

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Observation f9656f08-209d-4349-9bb7-397ab757a20d · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 30

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.782262Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a7ad3bd6-8c4d-4388-afc6-8bad09d04bae · outbound

This paper cites an unresolved cited work.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Unresolved cited work

Reference 31

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.761500Z

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Observation e023e884-049d-4437-bbc3-cc3076b848bd · outbound

This paper cites doi: https://doi.org/10.1016/j.media.2022.102479.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution doi: https://doi.org/10.1016/j.media.2022.102479

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:45:33.772565Z

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Observation 55d8f19b-c38a-4653-a8e5-47b9a631c119 · outbound

This paper cites Solving inverse problems in medical imaging with score-based generative models.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Solving inverse problems in medical imaging with score-based generative models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.020267Z

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Observation 90963393-1bd7-4958-97dd-5485ff613ed2 · outbound

This paper cites ¨Ozbey, O.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution ¨Ozbey, O

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T15:45:33.768636Z

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Observation e282571d-b121-496e-b77f-086ca65a73b0 · outbound

This paper cites SwinIR: Image restoration using Swin transformer.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution SwinIR: Image restoration using Swin transformer

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.026563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb08a2a8-b298-4921-8d26-7300fdc6160b · outbound

This paper cites Denoising diffusion probabilistic models.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Denoising diffusion probabilistic models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.035906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c954ff92-c79d-497d-a1ce-9fab8c0ea80e · outbound

This paper cites Denoising diffusion implicit models.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Denoising diffusion implicit models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.045739Z

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Observation 6406824a-eec1-4ded-9535-b2d8b9d0840b · outbound

This paper cites Inversion by direct iteration: An alternative to denoising diffusion for image restoration.Transactions on Machine Learning Research (TMLR).

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Inversion by direct iteration: An alternative to denoising diffusion for image restoration.Transactions on Machine Learning Research (TMLR)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.010273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64bca4f2-f57d-4755-9f15-99cd1b92e380 · outbound

This paper cites Kleinberg, and Samy Bengio.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Kleinberg, and Samy Bengio

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.029389Z

Source-reported events for the cited work

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Observation dea7ed71-35be-41aa-a163-e026e9ffb608 · outbound

This paper cites MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders, June 2025.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders, June 2025

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.007088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed9e6af4-2ac8-4d60-be10-59e538cdee83 · outbound

This paper cites Masset, R.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Masset, R

Reference 41

Resolution: malformed identifier; doi_truncated, observed 2026-05-10T15:45:33.779027Z

Source-reported events for the cited work

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Observation 988b5833-d328-4545-91a3-063c96e251bc · outbound

This paper cites Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Anja Thieme, Matthew P.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Anja Thieme, Matthew P

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.003873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09aa6aa9-3e3e-4e8a-9b5f-a6b62d313e02 · outbound

This paper cites Exploring scalable medical image encoders beyond text supervision.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Exploring scalable medical image encoders beyond text supervision

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:01:01.560745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d23d535f-67bb-4ae2-bedc-d0afb0346fa7 · outbound

This paper cites Fleet, and Mohammad Norouzi.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Fleet, and Mohammad Norouzi

Reference 44

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T10:01:01.546627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 527683a0-455f-4d33-af14-347183078887 · outbound

This paper cites DiffIR: Efficient diffusion model for image restoration.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution DiffIR: Efficient diffusion model for image restoration

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.042798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 840d0197-2ff0-4d61-aadb-2a47dda0402c · outbound

This paper cites The perception-distortion tradeoff.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution The perception-distortion tradeoff

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:07.997286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 336a03e2-629b-4282-9855-4ab2a5b06386 · outbound

This paper cites Generative modelling with inverse heat dissipation.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Generative modelling with inverse heat dissipation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.032246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9ded7cc-3b30-4be3-a404-2cdb7b06c13c · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.039699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7406ed32-5152-4dc7-98d1-0f0cce40f814 · outbound

This paper cites Science , author =.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Science , author =

Reference 49

Resolution: metadata mismatch; doi, observed 2026-05-10T15:45:33.831499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70aeb350-5fca-46ad-933c-11583e83f52a · outbound

This paper cites McCradden, Kathleen Creel, Ronald Boellaard, Eliza- beth C.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution McCradden, Kathleen Creel, Ronald Boellaard, Eliza- beth C

Reference 50

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.837841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c03a48b-d31f-44a2-9e26-e6b7bb80dfe9 · outbound

This paper cites Practical and Ethical Considerations for Generative AI in Medical Imaging.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Practical and Ethical Considerations for Generative AI in Medical Imaging

Reference 51

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.855945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6bfdad8-f503-46c0-b639-ae0c1a5fd146 · outbound

This paper cites Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N

Reference 52

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.834680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04cc587c-f221-44ca-9f5c-5ea753039c83 · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:29:58.212702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f16bed2c-85ae-4a7a-bd40-e6da978bf48a · outbound

This paper cites MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:16:17.195024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e1a257c-1fb1-4a3e-a56e-33e9c0c6c946 · outbound

This paper cites Arabboev, S.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Arabboev, S

Reference 55

Resolution: metadata mismatch; doi, observed 2026-05-10T15:45:33.828063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72ffd5f8-6221-441e-be0b-44ec4c5a21a9 · outbound

This paper cites Klemens, Ivo M.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Klemens, Ivo M

Reference 56

Resolution: verified exact; doi, observed 2026-05-10T15:45:33.825260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a708d76-fb37-4b02-83e5-b218852315fb · outbound

This paper cites Efros, Eli Shechtman, and Oliver Wang.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Efros, Eli Shechtman, and Oliver Wang

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:07.994091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f5d57ad5-4e70-4956-af20-375831987421 · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local Nash equi- librium.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution GANs trained by a two time-scale update rule converge to a local Nash equi- librium

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:10:08.000896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4fc4eb5-a6ed-46b7-baf9-f10caebbbe82 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models, June 2022.

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution Progressive Distillation for Fast Sampling of Diffusion Models, June 2022

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:01:01.524093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Reference resolution

Outbound references

Pith citing papers