Paper Citation Record · LEDGER

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation

As of 18 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2508.11032.

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

pith.paper-citation-record.v1
2508.11032 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:34:47.368395Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T14:34:20.894720Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T14:37:03.091990Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 4aa717bf-bdb1-4627-87e4-7a29f58ab41f · outbound

This paper cites Evolutionary Optimization of Model Merging Recipes.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Evolutionary Optimization of Model Merging Recipes

Reference 1

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Observation f96e4f50-1657-41d6-82d6-d4aa80a1c377 · outbound

This paper cites Evolutionary optimization of model merging recipes.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Evolutionary optimization of model merging recipes

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:48.014622Z

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Observation f6dcdd5a-9696-4d1b-8e01-842f49e2ed2d · outbound

This paper cites Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy

Reference 3

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Observation b813d598-18cf-4136-8f83-b92105faffb6 · outbound

This paper cites MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks

Reference 4

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Observation 40de7617-6e42-40fb-9aca-6e2f3ee96270 · outbound

This paper cites The medical segmentation decathlon.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation The medical segmentation decathlon

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:48.002090Z

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Observation 4ca5daea-0cc5-430f-b730-2100b9fefe3a · outbound

This paper cites Medicosam: Towards foundation models for medical image segmentation.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Medicosam: Towards foundation models for medical image segmentation

Reference 6

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Source-reported events for the cited work

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Observation cf91724b-4b50-4ebf-a4e4-3a3e1454eb55 · outbound

This paper cites Random forests.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Random forests

Reference 7

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Observation 19aedce0-21f9-4d77-bf64-e204ea870b51 · outbound

This paper cites Universeg: Universal medical image segmentation.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Universeg: Universal medical image segmentation

Reference 8

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Observation a12ac092-decf-4a98-93d4-13ba2b9369f9 · outbound

This paper cites Neuralizer: General neuroimage analysis without re-training.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Neuralizer: General neuroimage analysis without re-training

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.974418Z

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Observation 68b65c70-8abc-4a87-a85b-b19321609f49 · outbound

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

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 59bc276e-c8fe-4496-b756-563eaa8a5b3a · outbound

This paper cites Parameter competition balancing for model merging.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Parameter competition balancing for model merging

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.960996Z

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Observation 0f0bacb4-2568-4469-a170-4798d070d631 · outbound

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

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Show and segment: Universal medical image segmentation via in-context learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.948189Z

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Observation 51a4b81e-d1f8-47af-8f43-258d28673aa1 · outbound

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

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Icl-sam: Synergizing in-context learning model and sam in medical image segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.934983Z

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Observation 931c24b2-f277-44f3-adc0-7ab0d0cf8150 · outbound

This paper cites Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D

Reference 14

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Observation 6ae416ec-2fb4-4b40-8e61-923c2b496f01 · outbound

This paper cites Editing Models with Task Arithmetic.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Editing Models with Task Arithmetic

Reference 15

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Observation 3c362cbb-740d-48fa-a41f-0fad1f36cd49 · outbound

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

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 16

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Observation 45bece7a-0e4b-4239-b8fe-b6bead1c7daa · outbound

This paper cites Whitwell, Chadwick Ward, et al.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Whitwell, Chadwick Ward, et al

Reference 17

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Observation b789b54d-7543-41bc-ba00-7669c115d24d · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 18

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Observation fd2d6dac-d03e-4a69-a28e-40c56e137a2d · outbound

This paper cites Measuring catastrophic forgetting in neural networks.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Measuring catastrophic forgetting in neural networks

Reference 19

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Observation 16526007-02fb-4d2b-9339-f909747891c3 · outbound

This paper cites Segment anything.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Segment anything

Reference 20

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Observation 4bbf5840-c14f-4a53-b92a-c4c150c26e2d · outbound

This paper cites Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

Reference 21

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Observation 5bad0e6b-68f9-47db-b725-693a4e2ea4f6 · outbound

This paper cites Domain generalization for medical imaging classification with linear-dependency regularization.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Domain generalization for medical imaging classification with linear-dependency regularization

Reference 22

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Observation d9984c44-ece3-496a-901a-8f6eb744f5f6 · outbound

This paper cites Smac3: A versatile bayesian optimization package for hyperparameter optimization.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Smac3: A versatile bayesian optimization package for hyperparameter optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.876483Z

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Observation 5c433b85-c94c-4236-9fa4-2a25ab9ae3a1 · outbound

This paper cites Evaluation of prostate segmentation algorithms for mri: the promise12 challenge.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Evaluation of prostate segmentation algorithms for mri: the promise12 challenge

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.863488Z

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Observation 1df04f34-0571-4be1-bb9f-3e43139ac6b9 · outbound

This paper cites Rethinking abdominal organ segmentation (raos) in the clinical scenario: A robustness evaluation benchmark with challenging cases.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Rethinking abdominal organ segmentation (raos) in the clinical scenario: A robustness evaluation benchmark with challenging cases

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.851515Z

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Observation e2198148-6aaa-4a0a-ad08-f49ef2c7bf38 · outbound

This paper cites Segment anything in medical images.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Segment anything in medical images

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.839463Z

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Observation 5f42d0be-7731-4c62-89ed-d7b618011cd8 · outbound

This paper cites Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the flare22 challenge.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the flare22 challenge

Reference 27

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Observation a0a2b2a5-ca97-43d8-ac3b-bcdab8b3c84a · outbound

This paper cites u ller, Christof von Kalle, Jochen S Utikal, Verena M \.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation u ller, Christof von Kalle, Jochen S Utikal, Verena M \

Reference 28

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Observation 6ad1b977-74da-4289-b7d4-38bd7e352461 · outbound

This paper cites Merging models with fisher-weighted averaging.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Merging models with fisher-weighted averaging

Reference 29

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Observation d655f738-bcb1-4b9e-813f-0c82c902834e · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats).

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation The multimodal brain tumor image segmentation benchmark (brats)

Reference 30

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Observation 5b4499c1-3823-4fca-9497-409ce9d26bbf · outbound

This paper cites What is being transferred in transfer learning? Advances in neural information processing systems, 33: 0 512--523, 2020.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation What is being transferred in transfer learning? Advances in neural information processing systems, 33: 0 512--523, 2020

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.785810Z

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Observation 8e45156d-7c6f-45fd-b37a-2df1b4b881a5 · outbound

This paper cites Dynammo: Dynamic model merging for efficient class incremental learning for medical images.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Dynammo: Dynamic model merging for efficient class incremental learning for medical images

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.772589Z

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Observation 6627c8dd-c99c-4cab-b195-f46795484ed0 · outbound

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

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Tyche: Stochastic in-context learning for medical image segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.760007Z

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Observation 92d208a3-86b8-4321-adac-5bfd9778098a · outbound

This paper cites A preclinical micro-computed tomography database including 3d whole body organ segmentations.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation A preclinical micro-computed tomography database including 3d whole body organ segmentations

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.747755Z

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Observation d8fba3c5-cdcd-4196-9809-69ba16fa6217 · outbound

This paper cites Fissionfusion: fast geometric generation and hierarchical souping for medical image analysis.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Fissionfusion: fast geometric generation and hierarchical souping for medical image analysis

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.735891Z

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Observation 3ba8d0f4-10cc-4a1a-904b-50ea9c6d21a4 · outbound

This paper cites Ridge-based vessel segmentation in color images of the retina.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Ridge-based vessel segmentation in color images of the retina

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.723824Z

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Observation 3a88e1ab-ae8c-484f-b2b3-d18b15ba8061 · outbound

This paper cites Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging

Reference 37

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Observation f755c559-8284-41a5-9578-85ce8f80ccb8 · outbound

This paper cites GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching

Reference 38

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Observation 9123d17c-2290-4e50-95ca-3ee94c8e2897 · outbound

This paper cites In-context learning for medical image segmentation.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation In-context learning for medical image segmentation

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:34:47.445174Z

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Observation 2d50ab0e-5f5e-4cfe-a379-4aff452b4975 · outbound

This paper cites Weight averaging for neural networks and local resampling schemes.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Weight averaging for neural networks and local resampling schemes

Reference 40

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Observation 777945d3-5fdd-4f3f-92d5-61e8ad0f3975 · outbound

This paper cites Sam-med3d: towards general-purpose segmentation models for volumetric medical images.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Sam-med3d: towards general-purpose segmentation models for volumetric medical images

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.705176Z

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Observation 4af7d98c-b0fd-45d9-9973-60f8b7396913 · outbound

This paper cites Seggpt: Towards segmenting everything in context.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Seggpt: Towards segmenting everything in context

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.693568Z

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Observation bd915bca-cca0-4b3b-8c5f-46930d4d7ad5 · outbound

This paper cites Sampling Generative Networks.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Sampling Generative Networks

Reference 43

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Observation 891c4340-c271-40ba-af07-293f0535d596 · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Medical sam adapter: Adapting segment anything model for medical image segmentation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:47.341648Z

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Observation a1f2e5c0-6f0f-4b57-b70a-b21dc8b4965a · outbound

This paper cites Ties-merging: Resolving interference when merging models.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Ties-merging: Resolving interference when merging models

Reference 45

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Observation 6433b4f8-955f-4eeb-b489-1cc6b714f864 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 46

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Observation 2492ad50-8f5c-45b4-8dcf-9fdb79cdee9b · outbound

This paper cites Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.667708Z

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Observation 409a9a2e-1a8b-4723-a891-dd3e30ae5463 · outbound

This paper cites SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 48

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Observation 8f3ddb26-1a8a-4f78-8035-d6f65f85a33c · outbound

This paper cites Language models are super mario: Absorbing abilities from homologous models as a free lunch.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 49

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Observation 4c13ef75-0a60-48d2-9523-e703519abc8b · outbound

This paper cites Nasalseg: A dataset for automatic segmentation of nasal cavity and paranasal sinuses from 3d ct images.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Nasalseg: A dataset for automatic segmentation of nasal cavity and paranasal sinuses from 3d ct images

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.647646Z

Source-reported events for the cited work

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Observation b6fa20de-605e-45be-bbc6-d6cd058d4d0b · outbound

This paper cites Segmic: A universal model for medical image segmentation through in-context learning.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation Segmic: A universal model for medical image segmentation through in-context learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:47.633989Z

Source-reported events for the cited work

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Pith citing papers

Observation e6009001-c2dd-4822-aeaa-e3e3e642fe42 · inbound

SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation cites this paper.

SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:41:02.371935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 28c62e76-6cd7-4236-9ea4-67315d4cc8c3 · inbound

Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound cites this paper.

Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T14:37:03.093404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.