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Paper Citation Record · LEDGER

Improving Medical Image Generative Models with Fr\'echet Distance Loss

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.13300.

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pith.paper-citation-record.v1
2607.13300 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:39:39.720270Z

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Reference resolution

27 of 27 outbound references displayed

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Outbound references

Observation b1d2bda4-4075-4b4f-b471-37a556544c7b · outbound

This paper cites Scientific Data4, 170117 (2017).https://doi.org/10.1038/sdata.2017.117.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Scientific Data4, 170117 (2017).https://doi.org/10.1038/sdata.2017.117

Reference 1

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This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

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Observation cc6fa6b2-171a-4992-872b-313bd5bf7869 · outbound

This paper cites Medical Image Analysis84, 102680 (2023).https://doi.org/10.1016/j.media.2022.102680.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Medical Image Analysis84, 102680 (2023).https://doi.org/10.1016/j.media.2022.102680

Reference 3

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Observation cc640965-6be2-4886-a406-a141ef36a8ae · outbound

This paper cites Demystifying MMD GANs.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Demystifying MMD GANs

Reference 4

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Observation 4bc13743-90aa-4909-9c0d-068d9ba95f4a · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Improving Medical Image Generative Models with Fr\'echet Distance Loss MONAI: An open-source framework for deep learning in healthcare

Reference 5

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This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Improving Medical Image Generative Models with Fr\'echet Distance Loss GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 6

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Observation 2dbccc22-e90c-470d-9354-1e17d82d2274 · outbound

This paper cites In: Advances in Neural Information Processing Systems.

Improving Medical Image Generative Models with Fr\'echet Distance Loss In: Advances in Neural Information Processing Systems

Reference 7

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Observation 4286055d-eca6-4a79-9f38-d567bcd4b375 · outbound

This paper cites In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Improving Medical Image Generative Models with Fr\'echet Distance Loss In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 8

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Observation 88d36c5a-b2fc-4673-b77f-8daa870ec770 · outbound

This paper cites Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models

Reference 9

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Observation 4d94e7de-a56b-4f59-bc1e-625602a837f1 · outbound

This paper cites Medical Image Analysis110, 103943 (2026).

Improving Medical Image Generative Models with Fr\'echet Distance Loss Medical Image Analysis110, 103943 (2026)

Reference 10

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Improving Medical Image Generative Models with Fr\'echet Distance Loss Unresolved cited work

Reference 11

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Observation 09911a04-22cf-4425-8047-2138fa23024a · outbound

This paper cites MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs.

Improving Medical Image Generative Models with Fr\'echet Distance Loss MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs

Reference 12

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Observation fac36a8d-646d-43bc-be02-85763f5d0efa · outbound

This paper cites Backpropagating through Fr\'echet Inception Distance.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Backpropagating through Fr\'echet Inception Distance

Reference 13

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This paper cites Radiology: Artificial Intelligence4(5), e210315 (2022).https://doi.org/10.1148/ryai.210315.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Radiology: Artificial Intelligence4(5), e210315 (2022).https://doi.org/10.1148/ryai.210315

Reference 14

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Observation c511c9f5-5b12-446a-81a8-7bf79b38ed41 · outbound

This paper cites IEEE transactions on medical imaging34(10), 1993–2024 (2015).https://doi.org/10.1109/TMI.2014.2377694.

Improving Medical Image Generative Models with Fr\'echet Distance Loss IEEE transactions on medical imaging34(10), 1993–2024 (2015).https://doi.org/10.1109/TMI.2014.2377694

Reference 15

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Observation 548c54ff-386f-43b7-9fee-6c1016f9d9fc · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Improving Medical Image Generative Models with Fr\'echet Distance Loss U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 16

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Observation 37ab3ff8-ace4-4c26-80e1-c43ed70e4bf7 · outbound

This paper cites Improved Techniques for Training GANs.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Improved Techniques for Training GANs

Reference 17

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Observation 68f993c5-1eb2-48e8-ad4e-f76b27de7d8a · outbound

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Improving Medical Image Generative Models with Fr\'echet Distance Loss Progressive Distillation for Fast Sampling of Diffusion Models

Reference 18

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Observation 4e5f0d50-313d-4d13-a32b-8de9085dee0f · outbound

This paper cites MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis.

Improving Medical Image Generative Models with Fr\'echet Distance Loss MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis

Reference 19

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Observation 2e044372-c3db-4160-ab41-1a7ecf268ecf · outbound

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Improving Medical Image Generative Models with Fr\'echet Distance Loss Denoising Diffusion Implicit Models

Reference 20

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Observation 2046a8fa-f66d-4426-8db4-d6b1b5d99f2b · outbound

This paper cites In: 2016 IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR).

Improving Medical Image Generative Models with Fr\'echet Distance Loss In: 2016 IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR)

Reference 21

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This paper cites In: Medical Image Computing and Computer Assisted Intervention – MICCAI.

Improving Medical Image Generative Models with Fr\'echet Distance Loss In: Medical Image Computing and Computer Assisted Intervention – MICCAI

Reference 22

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This paper cites Sci- entific Data13(1), 31 (2025).https://doi.org/10.1038/s41597-025-06343-4.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Sci- entific Data13(1), 31 (2025).https://doi.org/10.1038/s41597-025-06343-4

Reference 23

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Observation 7ed2e3b0-4bb0-4ee8-9d91-a654fee8388e · outbound

This paper cites Representation Fr\'echet Loss for Visual Generation.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Representation Fr\'echet Loss for Visual Generation

Reference 24

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This paper cites Nature Communications16(1), 6486 (2025).https://doi.org/10.1038/ s41467-025-61754-6 Improving Medical Image Generative Models with Fréchet Distance Loss 11.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Nature Communications16(1), 6486 (2025).https://doi.org/10.1038/ s41467-025-61754-6 Improving Medical Image Generative Models with Fréchet Distance Loss 11

Reference 25

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This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Improving Medical Image Generative Models with Fr\'echet Distance Loss BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 26

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Improving Medical Image Generative Models with Fr\'echet Distance Loss Unresolved cited work

Reference 2024

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