Paper Citation Record · LEDGER

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation

As of 16 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2512.18176.

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

pith.paper-citation-record.v1
2512.18176 v2

Coverage vector

measured 54 of 54 reference resolution

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measured 55 of 55 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 2026-08-04T01:14:35.222900Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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Outbound references

Observation 27ded3cd-a709-431a-b039-6e714e4ab934 · outbound

This paper cites V oxelmorph: a learning framework for deformable medical image registration.IEEE Transactions on Medical Imaging, 38(8):1788–1800, 2019.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation V oxelmorph: a learning framework for deformable medical image registration.IEEE Transactions on Medical Imaging, 38(8):1788–1800, 2019

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:03.926867Z

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Observation a54c8952-39a1-4808-b340-3e2f72286be3 · outbound

This paper cites Fam- net: Frequency-aware matching network for cross-domain few-shot medical image segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Fam- net: Frequency-aware matching network for cross-domain few-shot medical image segmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.034470Z

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Observation 8b7c5187-3961-47df-a38c-9ca85e7dff02 · outbound

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

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Uni- verseg: Universal medical image segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.111759Z

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Observation 02d01091-63f4-4be6-93da-c0df356a38da · outbound

This paper cites Few-shot medical image segmenta- tion via generating multiple representative descriptors.IEEE Transactions on Medical Imaging, 43(6):2202–2214, 2024.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Few-shot medical image segmenta- tion via generating multiple representative descriptors.IEEE Transactions on Medical Imaging, 43(6):2202–2214, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.221846Z

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Observation c05911b6-4dbe-4d7e-b9ca-4bc18f5c77b2 · outbound

This paper cites Utnet: a hy- brid transformer architecture for medical image segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Utnet: a hy- brid transformer architecture for medical image segmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.307148Z

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Observation 6ad4401b-3b3e-4361-93a8-da42a3521122 · outbound

This paper cites Show and segment: Universal medical image segmentation via in-context learning.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Show and segment: Universal medical image segmentation via in-context learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.362810Z

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Observation 9d3cf86b-8cb6-4a87-a99a-e108ee138eb9 · outbound

This paper cites 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation.Medical Image Analysis, 98:103324, 2024.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation.Medical Image Analysis, 98:103324, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.468427Z

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Observation 2902dc6e-94e2-4501-b234-aa81388c31ad · outbound

This paper cites Synth- morph: learning contrast-invariant registration without ac- quired images.IEEE Transactions on Medical Imaging, 41 (3):543–558, 2021.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Synth- morph: learning contrast-invariant registration without ac- quired images.IEEE Transactions on Medical Imaging, 41 (3):543–558, 2021

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.578539Z

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Observation 2884bdd8-0f72-4bb0-8268-21f7e1a5099d · outbound

This paper cites Learning the ef- fect of registration hyperparameters with hypermorph.The Journal of Machine Learning for Biomedical Imaging, 1:003,.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Learning the ef- fect of registration hyperparameters with hypermorph.The Journal of Machine Learning for Biomedical Imaging, 1:003,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.690163Z

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Observation 7d04d478-5db3-4407-a7ab-fe11ef47bed7 · outbound

This paper cites Icl-sam: Synergizing in-context learning model and sam in medical image segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Icl-sam: Synergizing in-context learning model and sam in medical image segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.806616Z

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Observation f94f1895-97a5-4165-8a8d-94e7aa68bffc · outbound

This paper cites Multi-atlas seg- mentation of biomedical images: a survey.Medical Image Analysis, 24(1):205–219, 2015.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Multi-atlas seg- mentation of biomedical images: a survey.Medical Image Analysis, 24(1):205–219, 2015

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.872641Z

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Observation 905a0944-5171-40f8-ab0c-a223230ff7e7 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.Na- ture Methods, 18(2):203–211, 2021.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.Na- ture Methods, 18(2):203–211, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:04.981187Z

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Observation 86371618-2c2f-4547-80a5-4fd3860a4d12 · outbound

This paper cites nnInteractive: Redefining 3D Promptable Segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation nnInteractive: Redefining 3D Promptable Segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.045144Z

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Observation e3b82564-cd7a-4b3d-947f-c72a5c715020 · outbound

This paper cites Chaos challenge-combined (ct-mr) healthy abdominal organ segmen- tation.Medical Image Analysis, 69:101950, 2021.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Chaos challenge-combined (ct-mr) healthy abdominal organ segmen- tation.Medical Image Analysis, 69:101950, 2021

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.151423Z

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Observation f4ad1b9a-bad1-4440-a10e-623b3200b9e4 · outbound

This paper cites Segment any- thing.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Segment any- thing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.241498Z

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Observation b22049a9-3ce6-4662-baaf-1b049cbae29b · outbound

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

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.374982Z

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Observation 593c8a21-b271-488f-b469-6993ce00af78 · outbound

This paper cites Few shot medical image segmentation with cross attention transformer.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Few shot medical image segmentation with cross attention transformer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.532239Z

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Observation d8262efd-38e9-4a2c-9616-a9fce6430122 · outbound

This paper cites Swin-umamba†: Adapting mamba-based vision foundation models for medical image segmentation.IEEE Transactions on Medical Imaging, 2024.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Swin-umamba†: Adapting mamba-based vision foundation models for medical image segmentation.IEEE Transactions on Medical Imaging, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.673668Z

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Observation 4c272082-2f5b-428e-b8ac-a482372c629b · outbound

This paper cites Segment anything in context with vision foundation models.International Journal of Computer Vision, 133(10): 7460–7485, 2025.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Segment anything in context with vision foundation models.International Journal of Computer Vision, 133(10): 7460–7485, 2025

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.833162Z

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Observation c7df4dde-5a0b-4b63-8d3f-ed479c8640aa · outbound

This paper cites Robust one-shot segmentation of brain tissues via image-aligned style transformation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Robust one-shot segmentation of brain tissues via image-aligned style transformation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:05.982988Z

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Observation 5d19069e-b766-4e21-a583-c6cad3114ec0 · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1):654, 2024.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Segment anything in medical images.Nature Communications, 15(1):654, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.137694Z

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Observation 86cfd2f3-cb85-4d60-98af-6b0b1e657f69 · outbound

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

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation MedSAM2: Segment Anything in 3D Medical Images and Videos

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.258737Z

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Observation 7b5b67fe-15b9-42b7-a356-535d2862650c · outbound

This paper cites One polyp identifies all: One-shot polyp segmentation with sam via cascaded priors and iterative prompt evolution.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation One polyp identifies all: One-shot polyp segmentation with sam via cascaded priors and iterative prompt evolution

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.343055Z

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Observation e03ccd48-a447-4186-95ea-7a11e66a8a74 · outbound

This paper cites an unresolved cited work.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.407147Z

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Observation 8594a942-adbf-483a-9ce8-05badae6a18a · outbound

This paper cites Cross-domain few- shot segmentation via iterative support-query correspondence mining.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Cross-domain few- shot segmentation via iterative support-query correspondence mining

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.502173Z

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Observation b0bdb41d-bf19-4323-ac86-7dffe9682543 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.597680Z

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Observation 97436f78-4548-42fd-881e-1d8069490448 · outbound

This paper cites Self-supervised learning for few- shot medical image segmentation.IEEE Transactions on Medical Imaging, 41(7):1837–1848, 2022.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Self-supervised learning for few- shot medical image segmentation.IEEE Transactions on Medical Imaging, 41(7):1837–1848, 2022

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.655671Z

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Observation 0ed3d489-2aba-41e3-a520-ce37cc4b0051 · outbound

This paper cites Avt: Multicenter aortic vessel tree cta dataset collection with ground truth segmentation masks.Data in Brief, 40:107801, 2022.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Avt: Multicenter aortic vessel tree cta dataset collection with ground truth segmentation masks.Data in Brief, 40:107801, 2022

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.725173Z

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Observation deb6773a-3fae-402c-8fb1-fff09e29dbc2 · outbound

This paper cites Tyche: Stochastic in-context learning for medical image segmenta- tion.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Tyche: Stochastic in-context learning for medical image segmenta- tion

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.801033Z

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Observation bfd4f52b-8ad8-425c-839b-425bb7b33d99 · outbound

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

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation SAM 2: Segment Anything in Images and Videos

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.870320Z

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Observation 06f2da32-5d73-4984-8a0b-b9211ce9520e · outbound

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

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:06.969670Z

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Observation fa613a34-b14c-4b7d-b9c3-16f273791fba · outbound

This paper cites Vm-unet: Vision mamba unet for medical image segmentation.ACM Transactions on Multimedia Computing, Communications and Applications, 2024.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Vm-unet: Vision mamba unet for medical image segmentation.ACM Transactions on Multimedia Computing, Communications and Applications, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.100958Z

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Observation ca392704-a3e7-4a8e-804a-0e208bb4568d · outbound

This paper cites Few-shot medical image segmentation with high-fidelity prototypes.Medical Image Analysis, 100:103412, 2025.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Few-shot medical image segmentation with high-fidelity prototypes.Medical Image Analysis, 100:103412, 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.180649Z

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Observation 08ca1205-57c6-43d1-8fc5-1b57fe549749 · outbound

This paper cites Multi-atlas segmen- tation with joint label fusion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):611–623, 2012.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Multi-atlas segmen- tation with joint label fusion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):611–623, 2012

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.274904Z

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Observation 05e79466-c84a-4f67-8281-b52b3ab62fa7 · outbound

This paper cites Mixed trans- former u-net for medical image segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Mixed trans- former u-net for medical image segmentation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.375181Z

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Observation 0a88a34a-b04a-4761-b77a-982d343facfd · outbound

This paper cites Sam-med3d: A vision foundation model for general-purpose segmentation on volumetric medical im- ages.IEEE Transactions on Neural Networks and Learning Systems, 2025.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Sam-med3d: A vision foundation model for general-purpose segmentation on volumetric medical im- ages.IEEE Transactions on Neural Networks and Learning Systems, 2025

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.483262Z

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Observation 5ba16194-7d3e-4157-a72b-b57052b23b20 · outbound

This paper cites Seggpt: Towards segmenting everything in context.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Seggpt: Towards segmenting everything in context

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.550952Z

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Observation 3560364b-dbf9-4711-99b9-d563e8a7fae5 · outbound

This paper cites vesselfm: A foundation model for universal 3d blood vessel segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation vesselfm: A foundation model for universal 3d blood vessel segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.649249Z

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Observation ad383e35-e339-4aa1-9ba4-aa1a90494654 · outbound

This paper cites Scribbleprompt: fast and flexible interactive segmen- tation for any biomedical image.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Scribbleprompt: fast and flexible interactive segmen- tation for any biomedical image

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.716952Z

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Observation 20d8c8d0-e252-4749-a24c-bcb82c62c49e · outbound

This paper cites Multiverseg: Scalable interactive segmenta- tion of biomedical imaging datasets with in-context guidance.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Multiverseg: Scalable interactive segmenta- tion of biomedical imaging datasets with in-context guidance

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.857140Z

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Observation 90e1c7d4-5b67-4812-a25d-04a2b50c17af · outbound

This paper cites One-prompt to segment all medical images.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation One-prompt to segment all medical images

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:07.961285Z

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Observation 7ddcccfe-76a4-4ba4-9efc-e51faf1f2bc6 · outbound

This paper cites Eicseg: Universal medical image segmentation via explicit in-context learning.IEEE Transactions on Medical Imaging, 2025.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Eicseg: Universal medical image segmentation via explicit in-context learning.IEEE Transactions on Medical Imaging, 2025

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.059550Z

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Observation 6522f400-005a-47d4-b532-4e88b7596042 · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.128968Z

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Observation 5c1694ef-ccae-4975-b50e-25655818cca7 · outbound

This paper cites Unlocking the power of sam 2 for few-shot segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Unlocking the power of sam 2 for few-shot segmentation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.223940Z

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Observation 18cb0946-b6bd-4d24-8933-6d1a544839f2 · outbound

This paper cites Mapseg: Unified unsupervised domain adaptation for heterogeneous medical image segmentation based on 3d masked autoencoding and pseudo-labeling.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Mapseg: Unified unsupervised domain adaptation for heterogeneous medical image segmentation based on 3d masked autoencoding and pseudo-labeling

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.354541Z

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Observation 92bad3c1-1232-4198-8104-5555fd91ed24 · outbound

This paper cites Segmic: A universal model for medical image segmentation through in-context learning.Pattern Recognition, page 112179, 2025.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Segmic: A universal model for medical image segmentation through in-context learning.Pattern Recognition, page 112179, 2025

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.457812Z

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Observation 345805e4-bf6f-46dc-871d-ccc3f24461b5 · outbound

This paper cites nn- former: V olumetric medical image segmentation via a 3d transformer.IEEE Transactions on Image Processing, 32: 4036–4045, 2023.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation nn- former: V olumetric medical image segmentation via a 3d transformer.IEEE Transactions on Image Processing, 32: 4036–4045, 2023

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.553915Z

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Observation d8b463e8-2ebf-4998-b283-4aa4dd0bec4c · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.IEEE Transactions on Medical Imaging, 39(6):1856– 1867, 2019.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.IEEE Transactions on Medical Imaging, 39(6):1856– 1867, 2019

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.608109Z

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Observation da911141-eeba-4d3e-8c43-6c817840d1fa · outbound

This paper cites Maup: Training-free multi-center adaptive uncertainty-aware prompting for cross- domain few-shot medical image segmentation.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Maup: Training-free multi-center adaptive uncertainty-aware prompting for cross- domain few-shot medical image segmentation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.688424Z

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Observation 079d1b4b-96d2-4926-8aaf-2a12a092f374 · outbound

This paper cites Few-shot medical image segmentation via a region-enhanced prototypical transformer.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Few-shot medical image segmentation via a region-enhanced prototypical transformer

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.746091Z

Source-reported events for the cited work

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Observation 18d3e596-db3b-4195-a5dd-ac6a166be0f5 · outbound

This paper cites Different FMs exhibit varying sensitivities to prompt types (e.g., points, boxes, or dense masks) based on their pretraining objectives and architecture.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Different FMs exhibit varying sensitivities to prompt types (e.g., points, boxes, or dense masks) based on their pretraining objectives and architecture

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.837899Z

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Observation 657bc00c-8982-456c-a623-28ca7bae8ad6 · outbound

This paper cites an unresolved cited work.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.887415Z

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Observation 5be2cc5f-60a2-41b0-b519-90fb46d8b73f · outbound

This paper cites However, their 2D architectures struggle to effectively cap- ture the full spatial context required for accurate segmen- tation in 3D medical data.

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation However, their 2D architectures struggle to effectively cap- ture the full spatial context required for accurate segmen- tation in 3D medical data

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:08.964314Z

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Observation 8b6b7171-3ef2-4454-bbb8-393fc6bc3730 · outbound

This paper cites In our method, we employ a deep-learning- based test-time optimization strategy derived from V ox- elMorph [ 1].

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation In our method, we employ a deep-learning- based test-time optimization strategy derived from V ox- elMorph [ 1]

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T15:10:09.077524Z

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Pith citing papers

Observation 562b85a9-4dac-4745-82d6-264df8e38ab7 · inbound

Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice cites this paper.

Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T01:14:35.222900Z

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