Pith. sign in

Paper Citation Record · LEDGER

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2411.15576.

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

pith.paper-citation-record.v1
2411.15576 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:11:55.983057Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

53 of 53 outbound references displayed

  • verified exact5
  • verified fuzzy37
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0dd221eb-532e-4089-ab03-948f88fe3c7e · outbound

This paper cites A simple and robust frame- work for cross-modality medical image segmentation ap- plied to vision transformers.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training A simple and robust frame- work for cross-modality medical image segmentation ap- plied to vision transformers

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.799771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.743351Z digest=sha256:53475e2e7a6e4d2ebf4f6e75102ed79ce61aa8c946092386ffef709077584d76

Observation 1ab891b1-b7c6-45c6-b076-7aa077d5743d · outbound

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

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training MONAI: An open-source framework for deep learning in healthcare

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.748532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.748532Z digest=sha256:2f5c17c2bb09c1ffa204e55bef49e8e155ea9fc31fd967f93039395b68378c05

Observation c37ace31-b704-47ae-8393-97a72bb69980 · outbound

This paper cites Adversarial image synthesis for unpaired multi-modal cardiac data.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Adversarial image synthesis for unpaired multi-modal cardiac data

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.785811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.754022Z digest=sha256:996edb51e8491c3bbbe79e3bfda36d8105e42269e8d7771b12fbd3d9dfd29c08

Observation 5f360c1f-e3db-466d-a87e-d8a9c7b831f9 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.759461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.759461Z digest=sha256:eedb82f04d07fc580f9a680b669397c45605c964e881d31f58708e86af6b7ab7

Observation 2f5fceb1-6976-453b-b193-5513deb1dc00 · outbound

This paper cites Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.764828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.764828Z digest=sha256:4eb266efbb43cbfb824246ae0777f985a4d76cb3cb3c5c8d8072b4b9c04f1dc0

Observation 4f2c104c-ad52-49f2-a08e-c527d468c469 · outbound

This paper cites CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:11:56.168360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.769617Z digest=sha256:8dfa65013292458227a736c93e2cfaa0c31bb12b5168246d37e290a209e93312

Observation dc12c643-e7ae-4f82-8514-8111b0702f6a · outbound

This paper cites Cross-lingual lan- guage model pretraining.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Cross-lingual lan- guage model pretraining

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.771684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.775214Z digest=sha256:5f6bfaaf6bfdb432c9ded4e24c6d4fae810809c5ea46f62d3c21c7b11d3b404a

Observation 3896015a-25f1-4eec-8348-e735d7205bd1 · outbound

This paper cites ResViT: Residual vision transformers for multi-modal medical image synthesis.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training ResViT: Residual vision transformers for multi-modal medical image synthesis

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:11:56.146951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.780371Z digest=sha256:6282b7eee8d2ad326344039585ba6679c73e2bf933e57260f91ca3bf2ce7bf97

Observation 72a24a0f-0872-414e-ba94-cfc3d80fb6b8 · outbound

This paper cites Hyperdense- net: a hyper-densely connected cnn for multi-modal im- age segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Hyperdense- net: a hyper-densely connected cnn for multi-modal im- age segmentation

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.757050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.785697Z digest=sha256:49e188ce752da75adfb9c20782b622a429f0db14fe7bc1dfaf99f76581c559df

Observation 069f00b1-9038-4533-9ec1-72cdf5e89aa9 · outbound

This paper cites Unpaired multi-modal segmentation via knowledge distilla- tion.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Unpaired multi-modal segmentation via knowledge distilla- tion

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.742177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.789999Z digest=sha256:76d5a9ebc7382e504f64e2fb194ff17f558629b18d6fd05aac3b07e6204e4f21

Observation 6cb5d2db-1ba7-4bdf-9324-bfe47f184830 · outbound

This paper cites Multi- modal multi-stream unet model for liver segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Multi- modal multi-stream unet model for liver segmentation

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.726369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.794144Z digest=sha256:3b21ae69e7e1f364ace0678020285a898f2c3ea5b08a18cbd04141d807a3107f

Observation 3d646e8e-9940-4b05-81a1-1e1fb3574219 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.711508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.798565Z digest=sha256:8ebf3e5139fe03bb3b0b1097a5234698b3ef85613e391f70c8dbced7aee7133c

Observation a2187388-cfac-45cf-a282-c9c655a3b8e7 · outbound

This paper cites Unetr: Transformers for 3d med- ical image segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Unetr: Transformers for 3d med- ical image segmentation

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.697743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.802746Z digest=sha256:c80b7923322a094542c1037c471a81a2742be6d77c3287140634f8934b6e7bf3

Observation 8b70ee92-ee18-4d8e-b2c5-5deaf1c28461 · outbound

This paper cites Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmen- tation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmen- tation

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.683748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.806979Z digest=sha256:92561177ca144823aa09d9daab4d216c97ad288f1b486afc9120423005484282

Observation 6064175a-4e26-41f0-9e14-5ba777a48088 · outbound

This paper cites Unpaired cross-modality educed distillation (cmedl) for medical image segmentation.IEEE Transactions on medical imaging, 41(5):1057–1068, 2021.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Unpaired cross-modality educed distillation (cmedl) for medical image segmentation.IEEE Transactions on medical imaging, 41(5):1057–1068, 2021

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.669147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.811132Z digest=sha256:bca25cf1959e84587d433862b4ab31570165de440b0ebe2ffa33f0d316f2f379

Observation 6f4cbb3e-1597-46d7-aa30-3e1031fdac63 · outbound

This paper cites Biobert: a pre-trained biomedical language representation model for biomedical text mining.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Biobert: a pre-trained biomedical language representation model for biomedical text mining

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.815139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.815139Z digest=sha256:d462e768675cac639bbca14110a63fa7c9c56b8680fec10d6bc9ae896c5058c0

Observation c8a24972-8e58-4295-ba65-530782366070 · outbound

This paper cites AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.820153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.820153Z digest=sha256:b1964c900cf6501a8fb55052a1cc5477f9adb081cdaab1ee3fb0737ae8c807a2

Observation 27b62ccf-4249-400a-9b04-40ea8c3c97db · outbound

This paper cites Focalunetr: A focal transformer for boundary-aware prostate segmentation using ct images.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Focalunetr: A focal transformer for boundary-aware prostate segmentation using ct images

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.644947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.824925Z digest=sha256:41b95a13f5c4c6bcb905637ec6e89dc6d50a4e276e2696c25841fb21faf8d43d

Observation 7e5b7f78-f382-4670-b49a-3f9078502cbd · outbound

This paper cites Towards cross-modality medical image segmentation with online mutual knowledge distillation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Towards cross-modality medical image segmentation with online mutual knowledge distillation

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.630242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.829836Z digest=sha256:0d675c968023bfb6f1de04e77941a2169cc69d7311e89d4060b38dd6bd1ac2a1

Observation 4669c07f-4b5c-497a-bd4f-416014b5641f · outbound

This paper cites M- flag: Medical vision-language pre-training with frozen lan- guage models and latent space geometry optimization.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training M- flag: Medical vision-language pre-training with frozen lan- guage models and latent space geometry optimization

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.616647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.834788Z digest=sha256:c352481e7ba0f71b8ee15b79eb715bf96a6b771e46095c4b36a2d9dc8d547880

Observation 6ac713be-c552-44fc-bef8-62cd3ffaec51 · outbound

This paper cites A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmen- tation with limited annotations.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmen- tation with limited annotations

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.602595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.839332Z digest=sha256:efaa7e6e29ce2d57e1cdfb3638e37ec1abbda3d00b321fd106250d8f2f07f3fb

Observation 112ab9c9-ffe1-4b4d-8723-8cfc3a963244 · outbound

This paper cites Clip-driven universal model for organ segmentation and tumor detection.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Clip-driven universal model for organ segmentation and tumor detection

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.589145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.843466Z digest=sha256:16048bc2353cc839b9be66b419a24b3fb8a38be0950b7507ab2e9d7408e79414

Observation 27b52592-6150-4b84-a106-ad5173e12a6a · outbound

This paper cites Decoupled Weight Decay Regularization.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Decoupled Weight Decay Regularization

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.847595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.847595Z digest=sha256:44140eef96f87087e3b72f69aac522319c50372949b0e39d3ad71ab97f3b9b81

Observation 94be0a29-1f16-4ce2-ad04-c4b76b918058 · outbound

This paper cites Deep neural networks for medical image segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Deep neural networks for medical image segmentation

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.575535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.852290Z digest=sha256:19bef44dfd5bf98997f8c6dcbd1bf2abde78030c6b76bc25c1429a8124276a24

Observation 02d4c687-eb55-4327-859a-7854283252ca · outbound

This paper cites Mirror u-net: Marry- ing multimodal fission with multi-task learning for seman- tic segmentation in medical imaging.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Mirror u-net: Marry- ing multimodal fission with multi-task learning for seman- tic segmentation in medical imaging

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.561578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.856665Z digest=sha256:5b912d2f9674946f2c43d0e15523523cdfa6c646db5e0fb33197204655a52673

Observation 20f3e7d7-cdf1-400b-9867-bc0c540fec57 · outbound

This paper cites Direct comparison of mri and x-ray ct technologies for 3d imaging of root systems in soil: potential and challenges for root trait quantification.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Direct comparison of mri and x-ray ct technologies for 3d imaging of root systems in soil: potential and challenges for root trait quantification

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.547628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.861540Z digest=sha256:3ee2b468dc7c2cb66c746d8aba08ed5f04d6a34a2d13dd86b72ccb884214c7ec

Observation 77ceefa9-ca73-4fb2-a784-cffd7e6f3d6a · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.532821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.866927Z digest=sha256:6c4f1d52cbe199a113054529210fde5a76feca1c7c281df96e1e8566d8e328ba

Observation cb43e3cb-0999-4b40-b9f1-5077ed487d4b · outbound

This paper cites Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:11:56.097611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.871630Z digest=sha256:d98389bff386783d50620e15a27caac793b08774aee36a4b68c3e8302e0caf7e

Observation bd47a471-7326-44b7-8ac5-6ded49757386 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training U- net: Convolutional networks for biomedical image segmen- tation

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.518429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.876881Z digest=sha256:4727efcd06c893b61a02ebecce2fd0600dc92cfc775ea806a8fba236e44e6b9b

Observation 7e5f65bd-31d2-44ea-877c-36b2c689d1a4 · outbound

This paper cites Batch Normalization Embeddings for Deep Domain Generalization.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Batch Normalization Embeddings for Deep Domain Generalization

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:11:56.076145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.881977Z digest=sha256:347024b51dbefce90d2c86c676a736196984a1dea4c4d349400df09d3acf3e94

Observation 3ac823e2-e333-44cc-9972-7f6bbfa6b836 · outbound

This paper cites Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.886406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.886406Z digest=sha256:8b1e8c719cd94786fb1f53593c4b007e74e87aa0c10714cbf1d0307cdbbf81e3

Observation 018bd56e-a86c-4f02-8ee4-04972583c021 · outbound

This paper cites Conditional con- volutions for instance segmentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Conditional con- volutions for instance segmentation

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.504596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.890825Z digest=sha256:83fb32f84c18a52c9b8e962888696a2f86ce706fdab8d941a10535d0b28f5d45

Observation 85c6372c-e497-4a84-907e-25d4c83fd467 · outbound

This paper cites Tganet: Text-guided attention for improved polyp seg- mentation.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Tganet: Text-guided attention for improved polyp seg- mentation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.489261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.895164Z digest=sha256:06c13ff101fb73be8789d6fc4fc44cff288e73e4e652ad6688ea69a9d4501bfa

Observation d66d2c6a-ae6f-4ada-9f4c-a790d6415de2 · outbound

This paper cites Multi-modal learning from unpaired images: Application to multi-organ segmentation in ct and mri.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Multi-modal learning from unpaired images: Application to multi-organ segmentation in ct and mri

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.473579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.899621Z digest=sha256:342a226fab353efb50652eeec3db8b664fc0d7758c42253b78dff38cbb1f71f3

Observation b75cb62f-4b7f-40ef-bc9f-5e02d78d1030 · outbound

This paper cites Valindria, Nick Pawlowski, Martin Rajchl, Ioannis Lavdas, Eric O.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Valindria, Nick Pawlowski, Martin Rajchl, Ioannis Lavdas, Eric O

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.457367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.903923Z digest=sha256:7f9c3f9ce34de0e7d93b5a3996db2c91e8caab200216f8fb6f1906cd3587d920

Observation 15ae0ae3-b7ba-4203-9236-6e91d7744bf1 · outbound

This paper cites MedCLIP: Contrastive Learning from Unpaired Medical Images and Text.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.908374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.908374Z digest=sha256:85fd7c4679b22668f0386af73b630236440a7d8c32fd145a6594496443b3606a

Observation cadd9e12-3498-4c7d-979c-cfddb7c76596 · outbound

This paper cites Toward unpaired multi-modal medical image seg- mentation via learning structured semantic consistency.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Toward unpaired multi-modal medical image seg- mentation via learning structured semantic consistency

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.443160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.912726Z digest=sha256:f57d6221d6ea989223c05cf08b8428030a7f27caf33dac8dc509759dabaaa6f5

Observation cd8521ca-8ebb-48bb-b25a-4f1e0e20ead0 · outbound

This paper cites Dillman, Nehal A.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Dillman, Nehal A

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.428532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.916888Z digest=sha256:818021f13255536d482b35eff025e32951c4b8c28c22389b68f658407e9565a2

Observation 113682f6-e56f-4758-bbec-277d4d56fd6e · outbound

This paper cites Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.412983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.921795Z digest=sha256:63ef79ee6c557b065afb6c468a6cd19ac0b30c2126b0517ec2f2841eff7aceec

Observation 439eb2f7-9b67-45c5-bc72-9c3a2501da06 · outbound

This paper cites Modality-aware mutual learning for multi-modal medical image segmenta- tion.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Modality-aware mutual learning for multi-modal medical image segmenta- tion

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.926208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.926208Z digest=sha256:5fe70528b2fa30dfa0dd6918dd4bc46a1426f78a8e4b605f17d8486e7875bfc4

Observation 6ae68bfe-6a79-40b6-b1cc-976a8d5ac953 · outbound

This paper cites Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-At-Risk.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-At-Risk

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.388583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.930720Z digest=sha256:2303bab4000597a9ef16be5507ac30346c178e8d899f8b20db3e32e83154ddca

Observation 5db2ceba-89a1-4e4c-b0e2-55b0835502e3 · outbound

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

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.359298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.939620Z digest=sha256:0b022f39868aa12b9b1d9c387f90ffd71d8dea309ee601395ce38895c3997d6c

Observation ffc2a1cc-2a80-4837-ab21-6366c2cf2932 · outbound

This paper cites Translating and Segmenting Multimodal Medical V olumes with Cycle- and Shape-Consistency Generative Adversarial Network, Mar.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Translating and Segmenting Multimodal Medical V olumes with Cycle- and Shape-Consistency Generative Adversarial Network, Mar

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.343739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.943883Z digest=sha256:ae95b4e8400456e10268ed64476fa828cbffd04e890487cc94f6d78f620abaa0

Observation bcd79bd8-8b91-4d4a-8a78-e92c260d650b · outbound

This paper cites Cross-modality medical image detection and segmentation by transfer learning of shapel priors.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Cross-modality medical image detection and segmentation by transfer learning of shapel priors

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.328698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.952622Z digest=sha256:b83a0c2e0383d67e2a80b1c29c5512d287f1a80c83da8bef12fda65bbe817cdf

Observation 74bfb902-8acf-4aae-b0af-fe0bd41e24bc · outbound

This paper cites Ariadne’s thread: Using text prompts to improve segmentation of infected areas from chest x-ray images.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Ariadne’s thread: Using text prompts to improve segmentation of infected areas from chest x-ray images

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T14:11:55.957261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:11:55.957261Z digest=sha256:74dc5fdf06de279184d9ce3207d79256411676a8584f1b72a80077df53497643

Observation 52758edd-e2cf-4f8a-9063-f7ac0b7d2305 · outbound

This paper cites A review of deep learning in medical imaging: Imaging traits, 10 technology trends, case studies with progress highlights, and future promises.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training A review of deep learning in medical imaging: Imaging traits, 10 technology trends, case studies with progress highlights, and future promises

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.303312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.961604Z digest=sha256:61b7ef9248505ce95bc48e60f99de5782ed1fbc22c609f2c3d4c4b577f8b27ed

Observation 216d5da2-5517-4d28-9132-dc393e88e5ed · outbound

This paper cites Latent correlation representation learning for brain tumor segmentation with missing mri modalities.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Latent correlation representation learning for brain tumor segmentation with missing mri modalities

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.286402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.965916Z digest=sha256:987b779c0bfb1863c0ba8b2026a1d42392feeadde88cee87b34e45eeb560cd0c

Observation b5d00e43-6ecb-4ab0-b9ad-2a082c8c1467 · outbound

This paper cites A review: Deep learning for medical image segmentation using multi- modality fusion.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training A review: Deep learning for medical image segmentation using multi- modality fusion

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.271357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.970023Z digest=sha256:c350bf00aebbf60ebdb8fff8a23732f81ed94346601cdd891d65587f4cf41968

Observation 56394eec-2f9e-4cd5-8bd7-964db83cdd13 · outbound

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

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Generalizable cross-modality medical image segmentation via style augmentation and dual normalization

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.256143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.974200Z digest=sha256:47ddf7ce8dc835f82f851fdda160a8ce9aaafdb9b670708741886c08715561d0

Observation 6f2bff5f-6466-4998-96e7-057241d57fa1 · outbound

This paper cites Generalizable Cross-modality Medical Image Segmen- tation via Style Augmentation and Dual Normalization.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Generalizable Cross-modality Medical Image Segmen- tation via Style Augmentation and Dual Normalization

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.241278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.978931Z digest=sha256:0e3712c8ac8d37f81b71e1958bf1b2b71b6d19921327eaf6c1402dff6e615bf4

Observation 0d89a572-7cb8-450a-82a6-9404a2c66602 · outbound

This paper cites Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:11:56.226976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.983057Z digest=sha256:e2abea3813c22cd3aa34230e12605614963e59f2f7c2a6a5b9113b58f17d21dd

Observation 46f0c5cf-a787-4743-8f4c-5ab9b190a46f · outbound

This paper cites Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network

Reference 2019

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:11:56.024670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.948071Z digest=sha256:4f106433f912ce7ea91eb89e272ed66bfcdb757cf2a68d62a593c1f2ff503660

Observation b3bbb319-8474-48a0-b029-210a97cccdee · outbound

This paper cites an unresolved cited work.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Unresolved cited work

Reference 2022

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:11:56.374002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T14:11:55.935443Z digest=sha256:19bce042225ce62c16618ee256a3afdc8c9776d4b17434e182470101beb69985

Pith citing papers

No inbound Pith citation observations are available.