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Paper Citation Record · LEDGER

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA

As of 7 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2509.22476.

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

pith.paper-citation-record.v1
2509.22476 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:54:52.867135Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

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

60 of 60 outbound references displayed

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External citation measurements

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Outbound references

Observation aa038982-1a5f-46e4-a94a-d3c9c7077ae5 · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 1

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Observation aa84afbb-977d-4638-9fc4-5bb110725a65 · outbound

This paper cites Wasserstein generative adversarial networks.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Wasserstein generative adversarial networks

Reference 2

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Observation f6d6fbc6-e8da-4eb2-bbe9-e09eae0043e3 · outbound

This paper cites Verification of forecasts expressed in terms of probability.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Verification of forecasts expressed in terms of probability

Reference 3

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Observation 2c36278e-095c-481a-b9dd-e49dbf4536ef · outbound

This paper cites u ggemann, Christos Sakaridis, Tim Br \.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA u ggemann, Christos Sakaridis, Tim Br \

Reference 4

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Observation ea141e0e-cfed-4e00-baf1-9e4ae191be16 · outbound

This paper cites Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation

Reference 5

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Observation 479d9309-c14e-436d-8996-ab6b5dd8a239 · outbound

This paper cites Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation

Reference 6

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Observation 75cee1e4-a98c-435d-95b4-5a8faa11d361 · outbound

This paper cites A generalist framework for panoptic segmentation of images and videos.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA A generalist framework for panoptic segmentation of images and videos

Reference 7

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Observation f1302236-9fd8-442b-ba1a-002d7213b69a · outbound

This paper cites Exact histogram specification.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Exact histogram specification

Reference 8

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Observation 73907791-8056-4151-8057-d78a069c5b11 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Diffusion models beat gans on image synthesis

Reference 9

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Observation 005f23d5-d291-43c6-80df-4763a0d9cdd5 · outbound

This paper cites Scribble-based domain adaptation via co-segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Scribble-based domain adaptation via co-segmentation

Reference 10

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Observation 9f1444ea-7a8b-44b5-a717-63ea3bf71aab · outbound

This paper cites Curves and surfaces for CAGD: a practical guide.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Curves and surfaces for CAGD: a practical guide

Reference 11

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Observation 49baeec6-7c15-4125-811f-1c8163589032 · outbound

This paper cites Digital image processing.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Digital image processing

Reference 12

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Observation cad18b3b-fc1f-4440-8711-da293f0edd0f · outbound

This paper cites Generative adversarial nets.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Generative adversarial nets

Reference 13

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Observation 9ea53f09-d573-4e6a-b607-f9b3ce025a9e · outbound

This paper cites Denoising diffusion probabilistic models.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Denoising diffusion probabilistic models

Reference 14

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Observation bfd25a58-d6bc-4592-99a0-b939dadb52c5 · outbound

This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Cycada: Cycle-consistent adversarial domain adaptation

Reference 15

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Observation 655765d9-497e-4a78-a4fc-e6719d04646f · outbound

This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 16

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Observation 44ac9902-f9d9-4248-bb95-30b9fcb8aa93 · outbound

This paper cites Synseg-net: Synthetic segmentation without target modality ground truth.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Synseg-net: Synthetic segmentation without target modality ground truth

Reference 17

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Observation 02116995-4c42-48d5-aefa-f704791761e1 · outbound

This paper cites Ddp: Diffusion model for dense visual prediction.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Ddp: Diffusion model for dense visual prediction

Reference 18

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Observation 0534ab54-8a91-4317-94f3-c15e514e2191 · outbound

This paper cites Psigan: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation-based mri segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Psigan: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation-based mri segmentation

Reference 19

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Observation c91dc932-b386-4be8-ad5e-8c4667f212d2 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA A style-based generator architecture for generative adversarial networks

Reference 20

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Observation d36bfcb5-415b-4c46-911d-f564d6cf6944 · outbound

This paper cites Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation

Reference 21

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Observation 67cd0d94-f87d-44c3-b246-8f5a92874baf · outbound

This paper cites The brain tumor segmentation (brats) challenge 2023: focus on pediatrics (cbtn-connect-dipgr-asnr-miccai brats-peds).

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA The brain tumor segmentation (brats) challenge 2023: focus on pediatrics (cbtn-connect-dipgr-asnr-miccai brats-peds)

Reference 22

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Observation a057221e-d9f1-4e01-810c-4ed2d83d5682 · outbound

This paper cites Learning texture invariant representation for domain adaptation of semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Learning texture invariant representation for domain adaptation of semantic segmentation

Reference 23

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Observation c31fa185-dfc2-4d92-acf2-b0ce304a11bc · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault--workshop and challenge.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Miccai multi-atlas labeling beyond the cranial vault--workshop and challenge

Reference 24

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Observation 62b0c21e-9ceb-4aee-92eb-1187e4a34ca5 · outbound

This paper cites Confidence estimation using unlabeled data.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Confidence estimation using unlabeled data

Reference 25

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Observation 28d2a88e-0480-4db3-add3-5f57de26fdd9 · outbound

This paper cites Spatial diffusion for cell layout generation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Spatial diffusion for cell layout generation

Reference 26

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Observation 515908a2-53fd-47a2-ab27-531252b6f7ee · outbound

This paper cites Bidirectional learning for domain adaptation of semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Bidirectional learning for domain adaptation of semantic segmentation

Reference 27

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Observation bd392a35-fa42-4afe-9dc9-5f0f5932db4d · outbound

This paper cites Susan: segment unannotated image structure using adversarial network.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Susan: segment unannotated image structure using adversarial network

Reference 28

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Observation ea9771df-5ec5-4d64-9d41-f2b97cc7f23e · outbound

This paper cites Unsupervised image-to-image translation networks.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Unsupervised image-to-image translation networks

Reference 29

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Observation 3cbee20c-d4d6-4f91-9f7a-aa2bbba38994 · outbound

This paper cites Optimized bézier curve based intensity mapping scheme for low light image enhancement.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Optimized bézier curve based intensity mapping scheme for low light image enhancement

Reference 30

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Observation a76438c8-eb69-45c7-b23f-2ffb4b890d3c · outbound

This paper cites Least squares generative adversarial networks.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Least squares generative adversarial networks

Reference 31

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Observation 5b884f23-f66b-45c1-9f2e-505f900635e8 · outbound

This paper cites Confidence-aware learning for deep neural networks.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Confidence-aware learning for deep neural networks

Reference 32

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Observation f32e1ea9-f39f-4003-a099-8837d0758405 · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Obtaining well calibrated probabilities using bayesian binning

Reference 33

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Observation ddd826bd-258a-42a3-a076-17c988934782 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Improved denoising diffusion probabilistic models

Reference 34

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Observation ace266c8-6445-4ab8-97cd-2f74324cc75c · outbound

This paper cites Diffusion-based image translation with label guidance for domain adaptive semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Diffusion-based image translation with label guidance for domain adaptive semantic segmentation

Reference 35

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Observation 752a2246-b364-4836-84f2-cc797ac6a99f · outbound

This paper cites Loopda: Constructing self-loops to adapt nighttime semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Loopda: Constructing self-loops to adapt nighttime semantic segmentation

Reference 36

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Observation 38605cdd-853e-404d-81e6-2e9b81fe3020 · outbound

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Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Denoising diffusion implicit models

Reference 37

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Observation 979d286d-6c89-4063-b280-61f3abb940fe · outbound

This paper cites Learning to adapt structured output space for semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Learning to adapt structured output space for semantic segmentation

Reference 38

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source=arxiv_source observed=2026-08-04T14:54:50.586197Z digest=sha256:4d2fc407820b605d6c1ef169c605c4d76ecd4407e2684e017cf53563effa46b5

Observation 2f9592ce-56d1-4e8a-b385-f7ca76ab3d73 · outbound

This paper cites Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation

Reference 39

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source=arxiv_source observed=2026-08-04T14:54:50.689407Z digest=sha256:00c3bfeeb63570c8a300d6b6ac7b65807bf5b61a04bf89ded97ef3d04f213359

Observation 94f83548-d20d-410b-b5f6-71a2560e28bd · outbound

This paper cites Towards generic semi-supervised framework for volumetric medical image segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Towards generic semi-supervised framework for volumetric medical image segmentation

Reference 40

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source=arxiv_source observed=2026-08-04T14:54:50.804198Z digest=sha256:e5d7ccd0d74d8200aa6020f50a015d146869b54e6829c47c0c910e50385c6229

Observation edd59a31-605b-411e-b033-0fb0abe92f6f · outbound

This paper cites Cyclesgan: A cycle-consistent and semantics-preserving generative adversarial network for unpaired mr-to-ct image synthesis.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Cyclesgan: A cycle-consistent and semantics-preserving generative adversarial network for unpaired mr-to-ct image synthesis

Reference 41

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source=arxiv_source observed=2026-08-04T14:54:50.949987Z digest=sha256:73e7718703cbb5b2b7b0d02b722c0afb34f19e8de3474508881cce882ec74941

Observation 55418ce4-449b-4fcf-b707-22ee69fc0e52 · outbound

This paper cites Diffusion models for implicit image segmentation ensembles.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Diffusion models for implicit image segmentation ensembles

Reference 42

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source=arxiv_source observed=2026-08-04T14:54:51.070623Z digest=sha256:a3f2db323622ea08df7eb261644349ec7ff70d591f789a138ef3000ab081f9ea

Observation d1a0d4f6-aeb6-4d65-9b23-5b58abaf2117 · outbound

This paper cites Fpl+: Filtered pseudo label-based unsupervised cross-modality adaptation for 3d medical image segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Fpl+: Filtered pseudo label-based unsupervised cross-modality adaptation for 3d medical image segmentation

Reference 43

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source=arxiv_source observed=2026-08-04T14:54:51.331784Z digest=sha256:3a8eb127ce4eb8fa2a1301be1fc448fe5b5d2f0762c94970c138832af3293900

Observation 4686e25b-a58e-4eb5-befe-85216fac18f7 · outbound

This paper cites Medsegdiff: Medical image segmentation with diffusion probabilistic model.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Medsegdiff: Medical image segmentation with diffusion probabilistic model

Reference 44

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source=arxiv_source observed=2026-08-04T14:54:51.501123Z digest=sha256:13f7f457ad02420123c7a5703b4d5e58deda056915dcd2e25428073ff5f58d21

Observation b2f93d3d-c829-465f-b63c-59f8ceb75544 · outbound

This paper cites A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation

Reference 45

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source=arxiv_source observed=2026-08-04T14:54:51.630212Z digest=sha256:2653307a512d898227c6098ec83fdbcffe45a76cd80fb2c7b2277d74892fb7bb

Observation fd622066-adb5-4998-9693-0cbfbeed64f7 · outbound

This paper cites Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation

Reference 46

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source=arxiv_source observed=2026-08-04T14:54:51.687179Z digest=sha256:988fe6ac3dbd875d271cecf7e807263acbd8a82531fb0a849c7671a06c21d134

Observation 17953d70-9fa5-465e-a0d6-7b1f4f5fb9cd · outbound

This paper cites Topocellgen: Generating histopathology cell topology with a diffusion model.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Topocellgen: Generating histopathology cell topology with a diffusion model

Reference 47

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source=arxiv_source observed=2026-08-04T14:54:51.836909Z digest=sha256:276b5c8574cec1eeb49503debfcd7ace18f0529c0e54ea53616ef17c2ddbed32

Observation 53f8ab84-453b-4dea-9ad6-dbebde4d621e · outbound

This paper cites Fda: Fourier domain adaptation for semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Fda: Fourier domain adaptation for semantic segmentation

Reference 48

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source=arxiv_source observed=2026-08-04T14:54:51.945063Z digest=sha256:2518c46bb1f24b1e292fecc8959aa480b1c2a9cbfd7af5d60821ef880fdbf22d

Observation bc474204-811d-4324-9914-ccd51562e028 · outbound

This paper cites Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation

Reference 49

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source=arxiv_source observed=2026-08-04T14:54:52.018145Z digest=sha256:5a49b8573f3baf13005c306897e7b9d2f6a27cc7999965fc3b500366869c58cc

Observation 52806af0-1bb0-4423-afaf-0a8856bded23 · outbound

This paper cites Zero-reference deep curve estimation for low-light image enhancement.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Zero-reference deep curve estimation for low-light image enhancement

Reference 50

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source=arxiv_source observed=2026-08-04T14:54:52.096182Z digest=sha256:41d763062ac08a9425fc4011df02c0ecaa09313aed870fa5ef38d9a30c87a81d

Observation 06e9f63d-d1ee-47fb-a52b-28a19f7002d7 · outbound

This paper cites Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network

Reference 51

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source=arxiv_source observed=2026-08-04T14:54:52.156041Z digest=sha256:321572aae19db1a4e16cbc5841b8791ccfb0cbae133ebfcc852e1b248ee0733a

Observation 5b9528ee-06db-425c-b765-fb99ed2002b4 · outbound

This paper cites Unsupervised domain adaptation for semantic segmentation with pseudo label self-refinement.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Unsupervised domain adaptation for semantic segmentation with pseudo label self-refinement

Reference 52

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source=arxiv_source observed=2026-08-04T14:54:52.274796Z digest=sha256:815b43e3b449a270b2509598d0f327443c9a791b8936d986984d1ce3a76195d6

Observation 4d6f9a0d-3283-46a9-b18b-f1f0fce1a31c · outbound

This paper cites Generalizable cross-modality medical image segmentation via style augmentation and dual normalization.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Generalizable cross-modality medical image segmentation via style augmentation and dual normalization

Reference 53

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source=arxiv_source observed=2026-08-04T14:54:52.385089Z digest=sha256:f88c0665be1487ffa67954d23df3fbe43e53a230f800dc5ffe904fb0ac4ec777

Observation d99ac645-5392-4521-9382-b9f4e02985c3 · outbound

This paper cites Models genesis: Generic autodidactic models for 3d medical image analysis.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Models genesis: Generic autodidactic models for 3d medical image analysis

Reference 54

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source=arxiv_source observed=2026-08-04T14:54:52.442659Z digest=sha256:830f39185dc0e149e3b3459d8db820d3a12d450e5f993e555cd6c3eebccf2a08

Observation 21918567-6d06-4dd5-862f-10bde7535006 · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 55

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source=arxiv_source observed=2026-08-04T14:54:52.506993Z digest=sha256:c81bb370937f23d8ce4f767b1e74f61bdd955f016d3c76462e62ca1442bc4f6b

Observation 98fd5f90-c0a5-4057-ab37-94b024e49ad8 · outbound

This paper cites Multi-scale patch and multi-modality atlases for whole heart segmentation of mri.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Multi-scale patch and multi-modality atlases for whole heart segmentation of mri

Reference 56

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source=arxiv_source observed=2026-08-04T14:54:52.574335Z digest=sha256:9c497acee6661114921f6efefc76d3ecde07fbf95a3ee9317f28c923b6d3fb64

Observation 40d6a5aa-6492-49ab-80b0-1faf824f41a2 · outbound

This paper cites write newline.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA write newline

Reference 57

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source=arxiv_source observed=2026-08-04T14:54:52.648085Z digest=sha256:a36a124ea454ead19f137ab98dfb07132b3acff1725dad304a567b01078a87fb

Observation bac864a4-7869-4fcb-8a44-d5a350e976e2 · outbound

This paper cites @esa (Ref.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA @esa (Ref

Reference 58

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source=arxiv_source observed=2026-08-04T14:54:52.725607Z digest=sha256:e8fd452bb59cf7532078ab821e15a210f18caba5867dee0cd569df943b0c11c2

Observation 99ee0be9-a621-4b42-8166-3539536420a1 · outbound

This paper cites an unresolved cited work.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA Unresolved cited work

Reference 59

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source=arxiv_source observed=2026-08-04T14:54:52.789076Z digest=sha256:f3a7ddb082d54b5b191354b89412827767a05e008c39dcb086d589586e83dfd2

Observation a214f0a2-a522-48ad-ad40-2ef60a7f5b49 · outbound

This paper cites E Z "E U.

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA E Z "E U

Reference 60

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source=arxiv_source observed=2026-08-04T14:54:52.867135Z digest=sha256:ae7202d63623f7155c269769b204ffb6d9581ae75b54bdc11557b50d4054d790

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