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Paper Citation Record · LEDGER

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

As of 7 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

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:10:09.077524Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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source=pdf_text observed=2026-08-03T15:10:07.375181Z digest=sha256:894deeba8ce95aa71d4f642c7bdfba674ee9771a3dfff0904731962ecc552f16

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

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source=pdf_text observed=2026-08-03T15:10:07.483262Z digest=sha256:32bac10bb6bf6e21909678d67354c352caf4c552e77b35bbbc72ec7d425ac0ef

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

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source=pdf_text observed=2026-08-03T15:10:07.550952Z digest=sha256:3a5d08cc77de9a16c45c26282761e15466a4125a5c65be19b17b7c37fdbdb32b

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

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source=pdf_text observed=2026-08-03T15:10:07.649249Z digest=sha256:2b9459f2ad4d51eaf2816ad83303356a3c7d186419efd4790d3a0f758008fb3b

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

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source=pdf_text observed=2026-08-03T15:10:07.716952Z digest=sha256:8e0deff2bac7fd920b835ab75924f90b3dbc21961b855c7ed517d0adfd2b6ea4

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

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source=pdf_text observed=2026-08-03T15:10:07.857140Z digest=sha256:693f1296fcd936039dcd8aad7e4b6a9552cb0c7079c04655615d45c9c1f45ec6

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

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source=pdf_text observed=2026-08-03T15:10:07.961285Z digest=sha256:433ec6a00a4c9f4f6a1f7c6591d9e337d57ccd60edc39b1f862e98131cfa4ad1

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

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source=pdf_text observed=2026-08-03T15:10:08.059550Z digest=sha256:20454d747e87d22356bc28a8a9929bdf469201352467f9d5b1197742bd1ec052

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

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source=pdf_text observed=2026-08-03T15:10:08.128968Z digest=sha256:fb026b4dff01d6df29671928ac2140c1d1b5c25f49a5a672c2df51eea8fb74c7

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

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source=pdf_text observed=2026-08-03T15:10:08.223940Z digest=sha256:c7ef5dbc95c7e6d06527562ad0eaddad0dc1dc2a14184635bbf5fd7da978cddc

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

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source=pdf_text observed=2026-08-03T15:10:08.354541Z digest=sha256:63cb774a7c0eab31246a4044b26087ec5d9d716544405c9a7ad0521743d46732

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

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source=pdf_text observed=2026-08-03T15:10:08.457812Z digest=sha256:44782ed728eb8b8fd31e2c200f48d00d666641c379e5eb88e6f823eefdf9407e

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

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source=pdf_text observed=2026-08-03T15:10:08.553915Z digest=sha256:5c6ac4034f428b4a2e7d76f4c51fb17d990653836eef46a5c53edd1ea1d5cb06

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

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source=pdf_text observed=2026-08-03T15:10:08.608109Z digest=sha256:edec98084d792478cf8c593c330092d64a260526797c3dbb0baa32cc23652b49

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

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source=pdf_text observed=2026-08-03T15:10:08.688424Z digest=sha256:43dc6f8c87140ff5835faf4448ec5d806df416015944ee6db2fc672529ba71af

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

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source=pdf_text observed=2026-08-03T15:10:08.746091Z digest=sha256:7b2c4cda38ecce2781c5ae9b4f25fe9ef3180d4553d9d64bdc37888ac59f5844

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

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source=pdf_text observed=2026-08-03T15:10:08.837899Z digest=sha256:556bdf70dfcb5ebde55947572f292071119f37e08fe6572eb9340fed33bd1ebf

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

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source=pdf_text observed=2026-08-03T15:10:08.887415Z digest=sha256:ad41a289a8c7c305dee489565cfc1c88de791a7368280df0280fb905490781c4

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

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source=pdf_text observed=2026-08-03T15:10:08.964314Z digest=sha256:72507bbcfd0bde6f628ccd14165156b36db4d9ea88bae9645e99055194ec4d57

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]

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source=pdf_text observed=2026-08-03T15:10:09.077524Z digest=sha256:7c1102021a3ff04acbca475fbe5bf3724fc3359e940c53be1b815ab46b9a61f7

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

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source=pdf_text observed=2026-08-04T01:14:35.222900Z digest=sha256:2d7476147eb79ace3e7d6e923f4964b5cdf8fc80abe4285a5d950cea55e82c0f