Paper Citation Record · LEDGER

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation

As of 14 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2412.12660.

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

pith.paper-citation-record.v1
2412.12660 v1

Coverage vector

measured 48 of 48 reference resolution

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:19:09.053989Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:19:12.599870Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 8f144c1d-e898-4096-b118-e0665c4e7e22 · outbound

This paper cites GPT-4 Technical Report.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation GPT-4 Technical Report

Reference 1

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Observation 4717263d-2a16-4355-983f-0a0f9ea8ffc5 · outbound

This paper cites Medical image segmentation review: The suc- cess of u-net.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Medical image segmentation review: The suc- cess of u-net

Reference 2

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Observation 872486d2-2c32-4352-b3b0-6cc3260ed2ac · outbound

This paper cites Uni- verseg: Universal medical image segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Uni- verseg: Universal medical image segmentation

Reference 3

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Observation 997215a9-c1f2-43fc-93a1-72fc91f00241 · outbound

This paper cites Uni- verseg: Universal medical image segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Uni- verseg: Universal medical image segmentation

Reference 4

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Observation 94ac53bb-d81a-45a5-b50d-7d4d06db8982 · outbound

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

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 5

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Observation 79c3fc7d-81e8-4c02-a68b-84dfc8d6a7eb · outbound

This paper cites Seg- ment anything in 3d with nerfs.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Seg- ment anything in 3d with nerfs

Reference 6

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Observation 6ef80f74-2cfa-41fd-b9c6-f853f6d12008 · outbound

This paper cites Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers

Reference 7

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Observation 5eff2aec-d8fc-46e3-9595-6d5c636ca0ee · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 8

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Observation 5fa551b7-57ac-46e2-95fa-902bd4b79d5b · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recogni- tion.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Adaptformer: Adapting vision transformers for scalable visual recogni- tion

Reference 9

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Observation f7df7c34-5ed3-400b-a80e-5aa5a505c65c · outbound

This paper cites Recent advances and clin- ical applications of deep learning in medical image analysis.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Recent advances and clin- ical applications of deep learning in medical image analysis

Reference 10

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Observation 6c8dec1e-4f38-4b97-8c48-4053ab46f4ab · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Masked-attention mask transformer for universal image segmentation

Reference 11

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Observation 50676353-f573-4d4a-86c4-19ec17a09b33 · outbound

This paper cites SAM-Med2D.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation SAM-Med2D

Reference 12

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Observation 06dd09e1-34f8-459d-86e2-e187c612e838 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation 99004563-9135-4d4c-9d00-9627f2751088 · outbound

This paper cites Training like a medical resident: Context-prior learning toward universal medical image segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Training like a medical resident: Context-prior learning toward universal medical image segmentation

Reference 14

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Observation 233c8f18-022b-4dc5-9ffd-7216d29290ed · outbound

This paper cites One model is all you need: multi-task learning enables simultaneous histology image segmentation and classifica- tion.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation One model is all you need: multi-task learning enables simultaneous histology image segmentation and classifica- tion

Reference 15

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Observation 0cb0ee6e-ccf0-4a34-98d0-472b651dbbc1 · outbound

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

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Unetr: Transformers for 3d med- ical image segmentation

Reference 16

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Observation 7e7bbfd6-056e-4e79-afe1-ab52e4febcd2 · outbound

This paper cites The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 17

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Observation 111a26de-9b5f-4943-8413-541dbcc93904 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation

Reference 18

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Observation 0ef1a047-9d37-458e-91fa-99746d7a6c39 · outbound

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

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Reference 19

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Observation 3eb7666f-9f77-4764-9a56-2598b12c2566 · outbound

This paper cites Segment anything in high qual- ity.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Segment anything in high qual- ity

Reference 20

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Observation 6d4fcdc4-6dcb-41d7-af86-a073d14284b3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Adam: A Method for Stochastic Optimization

Reference 21

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Observation 8caff52b-6a7e-4fb1-b0c5-56e955eaf28b · outbound

This paper cites Segment any- thing.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Segment any- thing

Reference 22

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Observation 58f33b14-a68f-4f0e-8ec5-fb0c7a4b6cfd · outbound

This paper cites Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge

Reference 23

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Observation 1f402ecf-c2c4-4c38-9783-a38945d042df · outbound

This paper cites Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography

Reference 24

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Observation 4e653d3a-0491-4fbc-9d1d-b655fd22534f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 25

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Observation 355d9b27-a9b5-485a-b3c6-30d3ce359873 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Fully convolutional networks for semantic segmentation

Reference 26

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Observation e274c8c3-9c1f-4704-ab96-64aafd521c32 · outbound

This paper cites Image segmenta- tion using text and image prompts.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Image segmenta- tion using text and image prompts

Reference 27

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Observation 7e1e5a66-be46-4fa6-98cb-f3d44ac454f1 · outbound

This paper cites Segment anything in medical images.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Segment anything in medical images

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:56:45.773293Z

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Observation b498667d-4227-49cb-87cc-2ec81c3e23ac · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 29

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Observation c4429444-aedb-41ea-a73a-6ca92b250123 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation SAM 2: Segment Anything in Images and Videos

Reference 30

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Observation 3422dd32-f537-4198-9103-c59110c1c184 · outbound

This paper cites Segment anything, from space? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8355–8365, 2024.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Segment anything, from space? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8355–8365, 2024

Reference 31

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Observation 84116fcf-d361-4a53-9232-82cdc0b54f85 · outbound

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

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 32

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Observation 4e7f77a1-63ab-470b-bca8-4a07f4f52d6c · outbound

This paper cites Attention is all you need.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Attention is all you need

Reference 33

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Observation 84a2ed20-7f29-4850-8c26-fdf6b98dc6ae · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 34

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Observation dbdbbe66-dc58-43a4-90b3-cefb733a473d · outbound

This paper cites MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation

Reference 35

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Observation 6739ff85-b1ae-489a-a864-dabfea98d97c · outbound

This paper cites Uniseg: A prompt-driven universal segmenta- tion model as well as a strong representation learner.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Uniseg: A prompt-driven universal segmenta- tion model as well as a strong representation learner

Reference 37

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Observation d934adca-1bc4-4864-a674-930a0d1eba25 · outbound

This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Inpaint Anything: Segment Anything Meets Image Inpainting

Reference 38

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Observation 846e8229-d742-49b7-8ccd-10f51db440a2 · outbound

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

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Modality-aware mutual learning for multi-modal medical image segmenta- tion

Reference 39

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Observation 5f8b5228-2bde-4f57-9b4f-5e6f2b50c66f · outbound

This paper cites SAM-SP: Self-Prompting Makes SAM Great Again.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation SAM-SP: Self-Prompting Makes SAM Great Again

Reference 41

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This paper cites nn- former: V olumetric medical image segmentation via a 3d transformer.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation nn- former: V olumetric medical image segmentation via a 3d transformer

Reference 42

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Observation 3d431ec3-f230-47c7-ac79-77dfdccb702d · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation

Reference 43

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Observation cc1747b7-8380-4918-b7b1-0c4b6bd06a26 · outbound

This paper cites Text promptable surgical instru- ment segmentation with vision-language models.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Text promptable surgical instru- ment segmentation with vision-language models

Reference 44

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This paper cites Segment everything everywhere all at once.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Segment everything everywhere all at once

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:56:45.613364Z

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Observation 6d4b3fd9-c404-4c18-ac91-a65443f5e23d · outbound

This paper cites 6.1) as well as the text template we have used (Sec.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation 6.1) as well as the text template we have used (Sec

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:56:45.595047Z

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SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Unresolved cited work

Reference 47

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Reference 48

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Observation 757dbd7e-b75c-40f7-958c-2d284aae9b03 · outbound

This paper cites 1 Method Components Med2D SAWDpmt SAWDunpmt SAWDsem T2VSE CMSA-Loss DSC mDSC Baseline 65.45 - BaselineSem 64.56 65.11.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation 1 Method Components Med2D SAWDpmt SAWDunpmt SAWDsem T2VSE CMSA-Loss DSC mDSC Baseline 65.45 - BaselineSem 64.56 65.11

Reference 49

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Observation be031c43-e394-4dbc-874a-bb7ef5213292 · outbound

This paper cites The ablation study of our method (point prompts) on Med2D-16M datasets.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation The ablation study of our method (point prompts) on Med2D-16M datasets

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:56:45.518652Z

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Pith citing papers

Observation 297e938c-aa08-4745-8be3-a3342dc1a95d · inbound

MedSeg-R: Medical Image Segmentation with Clinical Reasoning cites this paper.

MedSeg-R: Medical Image Segmentation with Clinical Reasoning SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation

Reference 19

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