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 12 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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.743351Z digest=sha256:34be918e9736974be4a48f19dc7fcf8218a6391821866e5196fc5286c37fda0e

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=sha256:3dcd2a81999edabbe3fcf4d31a90590b36f78b93bd82900706590a503d23f31b

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.754022Z digest=sha256:8a3c2b3cb20bbbac7257c76ef644d20e562c0b8cdc10fd6160d8754bfa0de69f

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:a2889141e6c175dbd2d506a0fa8535312ae13eecb92637939158d3ebfd403a36

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:d18b00f13cdd51cd1e964fcd95301220f03f9e74a023e1cafeb6a38adf65814a

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.769617Z digest=sha256:ef421736abb78ad5ff01b8f0968a2a87fb9b8c5f253b9dd09f6d1489a2623645

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.775214Z digest=sha256:b13778baad1b7f6986c5b6c581eb3fd946b56a6b5f0945de160d6b439b3961e7

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.780371Z digest=sha256:59fc37b2ec3b57f632c313fcca50660e3e82de6e6a39686ee14e39b70ac9cf74

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.785697Z digest=sha256:98c4adf31abc7079036c877c7f19e64ac045dd074cd654b3d064684d4315df2d

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.789999Z digest=sha256:1814a2bc78c343a2581058646e055c66b6cf6c5788ded7b2991a4fb598f2d931

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.794144Z digest=sha256:c79a23fe8236983d015ac1bdf9d18202f605b5e5df7ad646b888d5433fc4a605

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.798565Z digest=sha256:4b575f4dddd8837514cffcc7ba10bf5e59668f6f9d1280224f63634483dce957

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.802746Z digest=sha256:e1fe70f6c176c741110866411094753af5b006203bc9bc9932e034cda9479eb5

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.806979Z digest=sha256:da5da8bd9d8bf57e48d28e26cc673103978e9e18021e939bc5e82a2ecb332592

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.811132Z digest=sha256:bd6589afb07c1cb95e8b42744cc5e0269cdf61c9ab8e36a5d80d6b8b0a7be8f3

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:df13484795d64866768d68b2854b0705382f52c26ac34ee2dd752426aae09805

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=sha256:03e81f655375c4154addf3e5d379d70c3af7b510cfb723436598f2e2371a4a70

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.824925Z digest=sha256:87536d9bebcaebe0a1f2036bf9b0b876c499e253fef149d2918abc12e85aab84

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.829836Z digest=sha256:720d0ec74f328ebb7c060e8d7365024291db5d0691bb4a0b72904113bfcd0ba0

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.834788Z digest=sha256:32dea04ff652c61ae303ddfde67027657e5f72af977553c621d0732a3f72d4a7

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.839332Z digest=sha256:57740104a966c3608404734e8f2aa92ff6bbf6086f42918bd3de663cfff2bd32

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.843466Z digest=sha256:225639279924f1199f0864afd16ee34f2a2344259d48ad6e95cd0722029c23d5

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=sha256:4e3935f4acd35ba5ebced15a6c21befa0275f8624171cd382040944bc0e10297

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.852290Z digest=sha256:dc343c32faeb756bf8ed67d244c2173005acc18c20652ccf680d0faf73021b58

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.856665Z digest=sha256:244c34f8439523d72abdaf7df35f173af9ab04b2984ae2d456aa2514d9492161

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.861540Z digest=sha256:baef0ab5c7a42dee4c1ddf713dc8232610b845b2b5fd7e40d2eddc5b7d5e03e0

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.866927Z digest=sha256:2fc805db210771e587e54e4549b1d771535701ce8e8f57cf5c1fb611509520b2

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.871630Z digest=sha256:9fb254f217a91334d598cc4abb8c3caa165aa70cecb9516933398746f7e3e977

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.876881Z digest=sha256:b38226ae7f9c694bf1a919639cd67474cb7762296aa18e20cbab3191b33238f5

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.881977Z digest=sha256:20c8e3e9d057221f4d75bec4a04bc8da6326d310172919da62fd32a0dafe8723

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=sha256:40ef34eede98710bb93cf2cf23c51c44345544bedde29995faaa49c1f293aab7

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.890825Z digest=sha256:cf696c55dddab93961e0dc0d36df2e4741612896f01d13bae46d3296a7e70aaa

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.895164Z digest=sha256:3db043a0aeb880ac2231910981f36a3d105c1c55a5e5973dc1f8d75eead043c8

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.899621Z digest=sha256:cc6ab8a529d6fb786813db3c99fb93efef2bcc733ecd1668cfbfe75547fc658a

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.903923Z digest=sha256:b093328ed788460f8da527d3f06a0d3f8b4bbb603f8c74d3cf57fb1a37a98575

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=sha256:9e0a6367b53ae476b0ba2387902f61660a3b4ab3f649f5b4bcb324dc1062c397

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.912726Z digest=sha256:3e0102224d38691aef08065fad4c4c86d70b7c3e8e93cd66033af3eda3d4f2ac

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.916888Z digest=sha256:4f63b01d80ddd290fd7e0f85748c5dd8da423b863c1d6f41ba934d94429b9721

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.921795Z digest=sha256:9f8fd27a06dac56a334ea6f309429d427cb7b75dc4d82aa0d53514a6129adaf7

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=sha256:53125f26f5e21bd204291662c99f4e6a242e953a618825b972ed426d596dc99e

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.930720Z digest=sha256:d13049d5841f968e51e3fa05c056f3cf1be183d9ed0ba9712dedd4e9d0114dc1

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.939620Z digest=sha256:6544f9ca48405f91896e5bf67304d05b4c87715e8043f0995dc37ee3c46eb9ea

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.943883Z digest=sha256:7141ce6282d2777cbb419c0ebf0cde2c53f52d7ec905a55126cbbc2f1be4df04

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.952622Z digest=sha256:a895e4f4112bff6c9b99318b086c0088548b21828435bc28554534b56523cde1

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=sha256:76dfdae271d190e9d8074c44c38aa68975d8962cf804e6b7085ee33422c9b65f

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.961604Z digest=sha256:81ecb386a15b3a808ae96edf3bc874811602b1637421df5a0fddde3b7fb0864a

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.965916Z digest=sha256:c057cd5aaa86729d99abbe735a65cdd41e556d1fdf1c6d4996fa138b24820e31

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.970023Z digest=sha256:22d22e7c3983ee4461a5defa1f24d37488394e1fa5ee027bfa9500c3b4231349

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.974200Z digest=sha256:c4320391251ccbcff15ac865ef13075fdfd0dfe1da9b7b0b89c875e4eab54c53

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.978931Z digest=sha256:85da3bd5a0f5d6c589b0c3d64144047b16a0082fde945cd994861d3446f7811e

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.983057Z digest=sha256:439c560482f3c736373e465920bbe4d5437f3d3512dffa96a3a1d2a6ac2b879c

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.948071Z digest=sha256:70a76086624d9b9251a36ee24c873ccf773ddda126aa0e47eb5956a6b20bfd88

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T14:11:55.935443Z digest=sha256:acb039bd2a5b0461e3c79359e7b61e2a3e90901571d5f06cbd96c66446d6f68e

Pith citing papers

No inbound Pith citation observations are available.