Paper Citation Record · LEDGER

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2509.06096.

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

pith.paper-citation-record.v1
2509.06096 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T04:32:24.210302Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:58:57.486072Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:19:47.443919Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d428830d-c841-4822-bb5a-91b475dae04c · outbound

This paper cites Mtlora: Low-rank adaptation approach for efficient multi-task learning,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Mtlora: Low-rank adaptation approach for efficient multi-task learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.946676Z

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Observation 5f1c5476-8ead-4130-bfe8-cbabed15d89a · outbound

This paper cites The medical segmentation decathlon,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation The medical segmentation decathlon,

Reference 2

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Observation eb212121-9753-4e48-bea3-ee9955c12b84 · outbound

This paper cites Medical image segmentation review: The success of u-net,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Medical image segmentation review: The success of u-net,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.921868Z

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Observation 4b376115-6363-4608-b548-4bbd73677bd2 · outbound

This paper cites The liver tumor segmentation benchmark (lits),.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation The liver tumor segmentation benchmark (lits),

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.907559Z

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Observation d40bb79f-0d5e-48b9-b65c-8234c9daf5c1 · outbound

This paper cites Efficient conditional pre-training for transfer learning,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Efficient conditional pre-training for transfer learning,

Reference 5

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Observation 57d7ac4e-2ff8-409f-9610-9ec995787e90 · outbound

This paper cites Conv-adapter: Exploring parameter efficient transfer learning for convnets,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Conv-adapter: Exploring parameter efficient transfer learning for convnets,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.877706Z

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Observation fc251e31-3a0f-405c-876e-caeb17bfd9d0 · outbound

This paper cites Expert tumor annotations and radiomic features for the ispy1/acrin 6657 trial data collection,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Expert tumor annotations and radiomic features for the ispy1/acrin 6657 trial data collection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.862851Z

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Observation 84099d1e-dd17-44e2-8f7b-d5e5265adb98 · outbound

This paper cites Efficient adaptation of large vision transformer via adapter re-composing,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Efficient adaptation of large vision transformer via adapter re-composing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.847852Z

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Observation c2745394-a6c1-41dc-9061-7f18743013c4 · outbound

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

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Training like a medical resident: Context-prior learning toward universal medical image segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.832986Z

Source-reported events for the cited work

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Observation 5b2914b3-8361-4a50-9709-a5f2866f38b8 · outbound

This paper cites Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.818899Z

Source-reported events for the cited work

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Observation 0b6bc8ae-95f6-418e-8579-41f478e264e0 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Masked autoencoders are scalable vision learners,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.804547Z

Source-reported events for the cited work

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Observation ba6a5452-c38b-4ded-8b4b-f54e25c23c51 · outbound

This paper cites Geometric visual similarity learning in 3d medical image self-supervised pre-training,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Geometric visual similarity learning in 3d medical image self-supervised pre-training,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.789089Z

Source-reported events for the cited work

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Observation 9eedffbf-8b61-4505-9bca-2d00ff6afc90 · outbound

This paper cites The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.772958Z

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Observation 0f5ce708-c482-4425-a8c8-e5a4bb4cca53 · outbound

This paper cites Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.757817Z

Source-reported events for the cited work

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Observation e42d9a51-fb8b-4f87-9123-987ec53f797d · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Parameter-efficient transfer learning for nlp,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.743433Z

Source-reported events for the cited work

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Observation ba012b10-2352-43f6-9a4c-f86a2da62c67 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation LoRA: Low-rank adaptation of large language models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.728266Z

Source-reported events for the cited work

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Observation f3dab8fb-b459-489b-bcf0-81892ef5e836 · outbound

This paper cites Fine-grained prompt tuning: A parameter and memory efficient transfer learning method for high-resolution medical image classification,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Fine-grained prompt tuning: A parameter and memory efficient transfer learning method for high-resolution medical image classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.712308Z

Source-reported events for the cited work

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Observation 24f28b1e-70fc-438f-9fd0-79dc55d603e7 · outbound

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

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 18

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Source-reported events for the cited work

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Observation 7261e789-caf0-436d-9d8d-76aa0e3662a7 · outbound

This paper cites Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.684507Z

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Observation 6845a00c-9c64-465c-8fac-b39886756a14 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Visualizing the loss landscape of neural nets,

Reference 20

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Observation b68cd897-118e-445c-92f3-49580720a275 · outbound

This paper cites Aligndet: Aligning pre-training and fine-tuning in object detection,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Aligndet: Aligning pre-training and fine-tuning in object detection,

Reference 21

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Observation 3df353a7-1ce7-4938-99f9-645a45c9125a · outbound

This paper cites Ccq: cross-class query network for partially labeled organ segmentation,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Ccq: cross-class query network for partially labeled organ segmentation,

Reference 22

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Observation 8578cc24-9112-483c-a772-09192ba0f865 · outbound

This paper cites Ditask: Multi-task fine-tuning with diffeomorphic transformations,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Ditask: Multi-task fine-tuning with diffeomorphic transformations,

Reference 23

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Observation d4a84dd6-585d-4627-bac2-1551fb1b8b09 · outbound

This paper cites Rapid artificial intelligence solutions in a pandemic—the covid-19-20 lung ct lesion segmentation challenge,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Rapid artificial intelligence solutions in a pandemic—the covid-19-20 lung ct lesion segmentation challenge,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.611196Z

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Observation ec293fcd-fb7d-4498-be87-bacafd464ae0 · outbound

This paper cites Med-tuning: A new parameter-efficient tuning framework for medical volumetric segmentation,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Med-tuning: A new parameter-efficient tuning framework for medical volumetric segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.596160Z

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Observation 967518bd-650b-4e27-b250-c5076b3e564f · outbound

This paper cites SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction

Reference 26

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Observation 56450a48-a7fc-48eb-8b74-1c888a4061d7 · outbound

This paper cites Self-supervised pre-training of swin transformers for 3d medical image analysis,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Self-supervised pre-training of swin transformers for 3d medical image analysis,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.581730Z

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Observation 0a39e1d3-5e16-408e-8524-ce4437d443c7 · outbound

This paper cites Hydralora: An asymmetric lora architecture for efficient fine-tuning,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Hydralora: An asymmetric lora architecture for efficient fine-tuning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.567318Z

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Observation 40bcce1b-af88-4094-82e9-7b5594c2f82f · outbound

This paper cites Training dataset for hntsmrg 2024 challenge,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Training dataset for hntsmrg 2024 challenge,

Reference 29

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Observation a4ef6b55-6878-4c0b-a2c6-86fd9e903805 · outbound

This paper cites Bridging the gap between recognition-level pre-training and commonsensical vision- language tasks,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Bridging the gap between recognition-level pre-training and commonsensical vision- language tasks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.552869Z

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Observation 1ceb0f40-eae6-49bc-87a3-5bc3c03b15a4 · outbound

This paper cites Task-robust pre-training for worst-case downstream adaptation,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Task-robust pre-training for worst-case downstream adaptation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.538547Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab21574d-9abb-48bb-94bd-541ebd139116 · outbound

This paper cites Large-Scale 3D Medical Image Pre-training with Geometric Context Priors.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Large-Scale 3D Medical Image Pre-training with Geometric Context Priors

Reference 32

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Observation 13137c67-cdb2-4306-a32e-18acad83b6d0 · outbound

This paper cites V oco: A simple-yet-effective volume contrastive learning frame- work for 3d medical image analysis,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation V oco: A simple-yet-effective volume contrastive learning frame- work for 3d medical image analysis,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.524110Z

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Observation cc977069-0cbb-45cd-87f4-3b4532efb4a9 · outbound

This paper cites Refs: A hybrid pre-training paradigm for 3d medical image segmentation,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Refs: A hybrid pre-training paradigm for 3d medical image segmentation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.509790Z

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Observation 229f4ab1-e781-4af7-b338-39a6da424aef · outbound

This paper cites Unimiss+: Universal medical self-supervised learning from cross-dimensional unpaired data,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Unimiss+: Universal medical self-supervised learning from cross-dimensional unpaired data,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.495175Z

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Observation a6697f8d-391e-4946-adaf-6fe9fbf37b9e · outbound

This paper cites MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:32:24.309767Z

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Observation e6ae955c-6211-4ade-80c4-496d2a94248a · outbound

This paper cites Continual self- supervised learning: Towards universal multi-modal medical data repre- sentation learning,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Continual self- supervised learning: Towards universal multi-modal medical data repre- sentation learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.480623Z

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Observation bba7d5a8-f7f6-4ad7-a86c-b2a518161a73 · outbound

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

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Uniseg: A prompt-driven universal segmentation model as well as a strong representation learner,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.465362Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 513cbd33-6554-4917-bb62-96dfbc857034 · outbound

This paper cites Desd: Self-supervised learning with deep self-distillation for 3d medical image segmentation,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Desd: Self-supervised learning with deep self-distillation for 3d medical image segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.450921Z

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Observation 3d5fdb86-af74-4eb4-bede-6d3abc985014 · outbound

This paper cites Cads: A self-supervised learner via cross-modal alignment and deep self-distillation for ct volume segmentation,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Cads: A self-supervised learner via cross-modal alignment and deep self-distillation for ct volume segmentation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.436542Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 817c040d-34ce-4c83-a7ef-c54e5b101914 · outbound

This paper cites 5%¿ 100%: Breaking performance shackles of full fine-tuning on visual recognition tasks,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation 5%¿ 100%: Breaking performance shackles of full fine-tuning on visual recognition tasks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.421012Z

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Observation 802e1cf5-68bd-4a0c-8d3a-51e9f18c49ac · outbound

This paper cites AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:32:24.287970Z

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Observation 5df40f0d-969c-423a-aa7a-432af4fb61e7 · outbound

This paper cites Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.406385Z

Source-reported events for the cited work

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Observation 23acad9a-0222-490a-86e0-6dc24b7ce7a2 · outbound

This paper cites Unified multi-modal di- agnostic framework with reconstruction pre-training and heterogeneity- combat tuning,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Unified multi-modal di- agnostic framework with reconstruction pre-training and heterogeneity- combat tuning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.392151Z

Source-reported events for the cited work

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Observation 973e4d76-8e2e-4a91-9878-fcf7dbc7925f · outbound

This paper cites Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:32:24.265798Z

Source-reported events for the cited work

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Observation 1b3650ec-2595-439c-b5e2-eaac3f67014d · outbound

This paper cites Models genesis,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Models genesis,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.377123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 685ee322-0254-46ed-9c26-ed358f2bafb4 · outbound

This paper cites Uni- perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks,.

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation Uni- perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:32:24.362205Z

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Pith citing papers

Observation 71d5a7f3-8bf5-4bc2-8ed9-f180688539c4 · inbound

C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model cites this paper.

C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:19:47.445337Z

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