Paper Citation Record · LEDGER

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation

As of 3 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2604.24876.

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

pith.paper-citation-record.v1
2604.24876 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T04:22:26.340374Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+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

33 of 33 outbound references displayed

verified exact6
verified fuzzy25
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f79c5fa2-66d7-4416-b5fc-20e1ab0940b1 · outbound

This paper cites Segment any- thing.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Segment any- thing

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.845919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 708535f9-5894-4f75-93ca-0b072e48fcb7 · outbound

This paper cites SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:43.181094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 39d84a32-4114-49d8-b609-9a5c14f5e1d9 · outbound

This paper cites Segment anything in medical images.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Segment anything in medical images

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.788702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation adf44b86-4ee1-4900-96da-1b121cb4f129 · outbound

This paper cites MedSAM2: Segment Anything in 3D Medical Images and Videos.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation MedSAM2: Segment Anything in 3D Medical Images and Videos

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:43.204490Z

Source-reported events for the cited work

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Observation 0811aeb5-bbb4-452c-8d7e-b92ed10ca149 · outbound

This paper cites Cat: Coordinating anatomical-textual prompts for multi-organ and tumor segmentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Cat: Coordinating anatomical-textual prompts for multi-organ and tumor segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.851282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 50018c15-5f03-46d9-9089-10f9bc019148 · outbound

This paper cites Large-Vocabulary Segmentation for Medical Images with Text Prompts.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Large-Vocabulary Segmentation for Medical Images with Text Prompts

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:43.316114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation fc25080e-3aea-4be5-bdf6-3c1881207d81 · outbound

This paper cites Text3dsam: Text- guided 3d medical image segmentation using sam- inspired architecture.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Text3dsam: Text- guided 3d medical image segmentation using sam- inspired architecture

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.802035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9a561bb8-cd9a-46f9-8ef8-d6b21e8ddb27 · outbound

This paper cites Effidec3d: An optimized decoder for high-performance and efficient 3d medical image segmentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Effidec3d: An optimized decoder for high-performance and efficient 3d medical image segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.808681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b641ae26-628b-4a9d-82e2-480f236dfd1f · outbound

This paper cites DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:46:43.213228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e1366be8-c0c0-417c-bd45-bbceaab3a80a · outbound

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

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation U-net: Con- volutional networks for biomedical image segmen- tation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.818118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 437ad58d-4220-4324-98d5-08a12a4bc1d3 · outbound

This paper cites 3d u-net: learning dense volu- metric segmentation from sparse annotation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation 3d u-net: learning dense volu- metric segmentation from sparse annotation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.791981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bca7a73c-4f91-44bb-b5a7-cc0837975a41 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T21:17:43.376137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b2387853-5205-4f91-8099-d0b63baaa448 · outbound

This paper cites Re- sunet++: An advanced architecture for medical image segmentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Re- sunet++: An advanced architecture for medical image segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.785683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4e696bde-ed3f-406d-9437-2dd5401f290e · outbound

This paper cites Uc- transnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Uc- transnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.848811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c0e4b531-a614-4554-9547-d03490dcb858 · outbound

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

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:46:43.320609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 092e7c9e-0064-4148-9413-3a19bc58a005 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.859594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f90af963-0138-4155-866e-598453d9b7e0 · outbound

This paper cites Swinunetr-v2: Stronger swin transform- ers with stagewise convolutions for 3d medical image segmentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Swinunetr-v2: Stronger swin transform- ers with stagewise convolutions for 3d medical image segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.853946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a54a5849-f252-40b0-becd-39e4628df74e · outbound

This paper cites Efficient MedSAMs: Segment Anything in Medical Images on Laptop.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:46:43.208696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3c7706af-4b0e-4985-a237-35728f91f78e · outbound

This paper cites Repvit-medsam: Ef- ficient segment anything in the medical images.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Repvit-medsam: Ef- ficient segment anything in the medical images

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.814775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f3df0320-079d-4897-a306-30c0d23ec445 · outbound

This paper cites Dsam: A faster sam for 3d med- ical image segmentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Dsam: A faster sam for 3d med- ical image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.833286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c3d33868-1c1a-4152-9a5b-a297513be1a6 · outbound

This paper cites Fastsam3d: An effi- cient segment anything model for 3d volumetric med- ical images.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Fastsam3d: An effi- cient segment anything model for 3d volumetric med- ical images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.795029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation cc7ad187-5308-4465-a07b-1a687bc1a98b · outbound

This paper cites Segvol: Uni- versal and interactive volumetric medical image seg- mentation.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Segvol: Uni- versal and interactive volumetric medical image seg- mentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.798370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8bb250f4-93d9-46de-82e5-f91d2992964f · outbound

This paper cites Image segmentation us- ing text and image prompts.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Image segmentation us- ing text and image prompts

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.830444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8cd341ba-eedc-4ec7-a950-bbed5627b7a3 · outbound

This paper cites A foundation model for joint segmentation, de- tection and recognition of biomedical objects across nine modalities.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation A foundation model for joint segmentation, de- tection and recognition of biomedical objects across nine modalities

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.837107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dbc575d9-97ea-4af1-af3c-608cf8488915 · outbound

This paper cites Bert: Pre-training of deep bidirectional transform- ers for language understanding.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Bert: Pre-training of deep bidirectional transform- ers for language understanding

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.856711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c09886e6-6e78-4746-aa7a-2b5938ab1db9 · outbound

This paper cites Gqa: Training general- ized multi-query transformer models from multi-head checkpoints.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Gqa: Training general- ized multi-query transformer models from multi-head checkpoints

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.843421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1d5733a6-c3d4-46e7-8281-379c490954f3 · outbound

This paper cites Roformer: Enhanced transformer with ro- tary position embedding.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Roformer: Enhanced transformer with ro- tary position embedding

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.821052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dcde2cd2-b3fb-4a07-94d2-6a22f5c0f42f · outbound

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

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation MONAI: An open-source framework for deep learning in healthcare

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:54:28.721435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d4702686-b0fe-45fe-9024-dc3d5641acc8 · outbound

This paper cites Opti- mized glycemic control of type 2 diabetes with rein- forcement learning: a proof-of-concept trial.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Opti- mized glycemic control of type 2 diabetes with rein- forcement learning: a proof-of-concept trial

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.805593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e172a3fd-7376-4641-bea7-efb2554bb205 · outbound

This paper cites A generalist medical language model for disease diagnosis assis- tance.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation A generalist medical language model for disease diagnosis assis- tance

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.827030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7c638753-df14-4aea-a1c9-d3147a307757 · outbound

This paper cites Lightweight transform- ers for clinical natural language processing.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Lightweight transform- ers for clinical natural language processing

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.824086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a94f27d9-f76a-42c5-9e8a-cb93d1b00fa1 · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Muon: An optimizer for hidden layers in neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.840492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation cd0ac782-d973-48fd-9cb2-f54ace0ea0d6 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parame- ters.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Deepspeed: System optimizations enable training deep learning models with over 100 billion parame- ters

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:08:02.811850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

No inbound Pith citation observations are available.