Paper Citation Record · LEDGER

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images

As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2507.00983.

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

pith.paper-citation-record.v1
2507.00983 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:06:39.267690Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:59:12.699824Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:59:13.251212Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6ee50eb1-4ae7-41ea-a2a7-b77d4c46490c · outbound

This paper cites Wang et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Wang et al

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:46.357030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5c66f17-2aa8-43a2-bbfa-5d1c3ec8dc0f · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:46.342093Z

Source-reported events for the cited work

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Observation 84f03955-f1b2-46a3-b1b5-e7e4a3fd4e72 · outbound

This paper cites Isensee et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Isensee et al

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:46.328082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 654273a2-83ec-4c97-a80d-4721a4a61fd0 · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:46.313791Z

Source-reported events for the cited work

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Observation 61879871-9ac1-49b3-a4c8-e35b80587d9e · outbound

This paper cites Ramesh, P.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Ramesh, P

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:46.292762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1568caf1-2073-4575-9a27-9fdd5a1adb71 · outbound

This paper cites Rombach, A.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Rombach, A

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:46.276149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 588025b9-6c6d-46d8-a21f-9f131a65b974 · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:46.238506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bb3437f9-4f81-4497-bfcc-005c6f5adda2 · outbound

This paper cites Goodfellow et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Goodfellow et al

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:45.981426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 287a1654-1b24-4fa8-9a0e-7e630a94a242 · outbound

This paper cites Kawar et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Kawar et al

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:45.665122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a8664a81-89cb-4a91-874a-0468881f9f71 · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:45.380256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b2d66bff-cf96-474f-9f44-4fae7bbdf7a2 · outbound

This paper cites Song et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Song et al

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:45.205076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8b8453c-6cfc-4750-99ea-f91cbf06bb43 · outbound

This paper cites Corrdiff: Corrective diffusion model for accurate mri brain tumor segmentation.IEEE Journal of Biomedical and Health Informatics, 28(3):1587, 2024.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Corrdiff: Corrective diffusion model for accurate mri brain tumor segmentation.IEEE Journal of Biomedical and Health Informatics, 28(3):1587, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:45.061464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2ad8592-9e6b-4140-adc1-c758cc2085d3 · outbound

This paper cites van den Bent, Thierry Gorlia, Wolfgang Wick, Martin Bendszus, and Klaus H.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images van den Bent, Thierry Gorlia, Wolfgang Wick, Martin Bendszus, and Klaus H

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:44.925865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5afb67c8-8553-49d8-8c9d-179c01717bcd · outbound

This paper cites Detection and localization of early-stage multiple brain tumors using a hybrid technique of patch-based processing, k-means clustering and object counting.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Detection and localization of early-stage multiple brain tumors using a hybrid technique of patch-based processing, k-means clustering and object counting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:44.781362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 27ddb4c4-d660-401c-832a-c2867c5efb23 · outbound

This paper cites Brennan, Holly J.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Brennan, Holly J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:44.601779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a109a114-5b9d-4bac-8dbf-42b9fea36dca · outbound

This paper cites Automated multi-class mri brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific Reports, 15(8114), 2025.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Automated multi-class mri brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific Reports, 15(8114), 2025

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:44.412070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2147823a-18b3-49dd-94e8-978944b17e92 · outbound

This paper cites Brain tumor segmentation from mri images using handcrafted convolutional neural network.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Brain tumor segmentation from mri images using handcrafted convolutional neural network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:44.233737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cdceefee-ca83-4fd4-b6bc-c911dcee2963 · outbound

This paper cites Chandramma, T.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Chandramma, T

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:37.071357Z

Source-reported events for the cited work

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Observation df8498b3-03be-449e-926a-5c8240043d48 · outbound

This paper cites Patterson, and Huixiao Hong.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Patterson, and Huixiao Hong

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:37.136432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed174704-673e-4954-b3c1-a58e647ffe61 · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:44.047020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 69ac3d3e-87f0-46b3-840e-b24a9f3e225b · outbound

This paper cites Liu et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Liu et al

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:43.840011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1a87b333-ce35-410a-bfa9-e4f827d9d69d · outbound

This paper cites Baid et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Baid et al

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:43.640995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6f0ce1f6-96c7-4f84-b06a-048785d6a142 · outbound

This paper cites Tutorial on variational autoencoders.arXiv preprint, 2016.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Tutorial on variational autoencoders.arXiv preprint, 2016

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:43.415622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2629444-4c79-4179-8a43-6c9f5d185baa · outbound

This paper cites Huang, J.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Huang, J

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:43.232369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b339d080-86ce-4a9d-bff4-105fb59bba63 · outbound

This paper cites 3d mri brain tumor segmentation using autoencoder regularization.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images 3d mri brain tumor segmentation using autoencoder regularization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:42.966681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 56a482a4-4499-43b3-b5df-4e74587e23ea · outbound

This paper cites A two-stage cascade model with variational autoencoders and attention gates for mri brain tumor segmentation.Brain- lesion, pages 435–447, 2020.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images A two-stage cascade model with variational autoencoders and attention gates for mri brain tumor segmentation.Brain- lesion, pages 435–447, 2020

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:37.827752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60a76fe7-d975-4646-b9db-74630c93352d · outbound

This paper cites Aswani and D.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Aswani and D

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:42.780469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 058262f1-a694-4142-9652-0a73140faff0 · outbound

This paper cites Drm-vae: A dual residual multi variational auto-encoder for brain tumor segmentation with missing modalities.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Drm-vae: A dual residual multi variational auto-encoder for brain tumor segmentation with missing modalities

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:42.392856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f857c23e-ccb3-49ca-9a8f-3e86724c17d7 · outbound

This paper cites Generative adversarial networks.arXiv preprint, 2014.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Generative adversarial networks.arXiv preprint, 2014

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:42.075837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b1b5a4c2-313d-4f31-b619-f6ad50486035 · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:41.767763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bccb507d-e789-4624-a940-4c9813dc4853 · outbound

This paper cites Generative adversarial network in medical imaging: A review.Medical Image Analysis, 2019.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Generative adversarial network in medical imaging: A review.Medical Image Analysis, 2019

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.261827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c36d394d-d643-42c9-9a5c-3c87b6d9061d · outbound

This paper cites Vox2vox: 3d-gan for brain tumour segmentation.arXiv preprint, 2020.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Vox2vox: 3d-gan for brain tumour segmentation.arXiv preprint, 2020

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.325440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d4b3b9d-02e9-41ba-9190-ce599274e085 · outbound

This paper cites Promptable counterfac- tual diffusion model for unified brain tumor segmentation and generation with mris.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Promptable counterfac- tual diffusion model for unified brain tumor segmentation and generation with mris

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.370526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33fd1fd6-5e5e-4393-a489-538d6b28cadc · outbound

This paper cites Pohl, and Yu Zhang.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Pohl, and Yu Zhang

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:41.503885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd6ccd2b-8bd6-4698-b0ec-805c12860b7d · outbound

This paper cites an unresolved cited work.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.489957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 599f87d8-c417-4643-a6bb-9e5246f1d008 · outbound

This paper cites Accelerating diffusion models via pre-segmentation diffusion sampling for medical image segmentation.arXiv preprint, 2022.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Accelerating diffusion models via pre-segmentation diffusion sampling for medical image segmentation.arXiv preprint, 2022

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.552403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f5a328e-4e12-4c78-ae2d-2ee178a09037 · outbound

This paper cites Recoseg: Residual guided cross-modal diffusion for efficient brain tumor segmentation.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Recoseg: Residual guided cross-modal diffusion for efficient brain tumor segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:41.207150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 762ab5b6-42e3-446b-93f1-8e15699ce5a2 · outbound

This paper cites Med- segdiff: Medical image segmentation with diffusion probabilistic model.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Med- segdiff: Medical image segmentation with diffusion probabilistic model

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.674350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b3da3bf-e7f1-4142-9163-078685120e82 · outbound

This paper cites Medsegdiff-v2: Diffusion based medical image segmentation with transformer.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Medsegdiff-v2: Diffusion based medical image segmentation with transformer

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:38.754995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6f503b0-62f1-47c7-9b12-5b95941d58d4 · outbound

This paper cites Segdiff: Image segmentation with diffusion probabilistic models.arXiv preprint, 2021.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Segdiff: Image segmentation with diffusion probabilistic models.arXiv preprint, 2021

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:40.931459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6136a53b-323e-4d0c-9eea-a5cdcaf9415b · outbound

This paper cites 3d u-net: Learning dense volumetric segmentation from sparse annotation.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images 3d u-net: Learning dense volumetric segmentation from sparse annotation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:40.715430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a40bcdc4-43b2-494d-92b1-de192a1db49f · outbound

This paper cites A multi brain tumor region segmentation model based on 3d u-net.Applied Sciences, 13(16):9282, 2023.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images A multi brain tumor region segmentation model based on 3d u-net.Applied Sciences, 13(16):9282, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:40.469913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 961bf11e-4cb1-4c79-a009-c8efc12c5db2 · outbound

This paper cites Improved denoising diffusion probabilistic models.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Improved denoising diffusion probabilistic models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:40.209078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11712e9a-513c-42c4-b3d1-617d390a3e0d · outbound

This paper cites Babu and K.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Babu and K

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:40.043867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c9edd01f-e126-44cf-ae87-08c9d545972d · outbound

This paper cites Sharma et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Sharma et al

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:39.968174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 675ef4bb-00de-4798-a494-cb0c96174de2 · outbound

This paper cites Xie et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Xie et al

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:39.716105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 44bf3cde-046b-4a7c-b800-248a674f6684 · outbound

This paper cites Nguyen et al.

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images Nguyen et al

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:39.496104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 1032519c-dca6-4a39-97e8-daea1531821b · inbound

ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation cites this paper.

ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:59:13.354218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.