Paper Citation Record · LEDGER

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.07602.

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

pith.paper-citation-record.v1
2507.07602 v1

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T18:42:39.043360Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b0e709ae-5443-4bd9-916c-446e8f85cff6 · outbound

This paper cites UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:42:40.838616Z

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Observation f1a663ee-ac98-4a05-a831-98393082fb69 · outbound

This paper cites End-to-end object detection with transformers.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning End-to-end object detection with transformers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:47.618348Z

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Observation c2b6b14b-dda8-4a1e-b476-251fcd46ea3b · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation d93047a8-1288-449e-a0e4-640190a13cb8 · outbound

This paper cites Deepncm: Deep nearest class mean classifiers.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Deepncm: Deep nearest class mean classifiers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:47.115635Z

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Observation 64419777-f3b4-4f06-acfb-f186b72ddfc9 · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmenta- tion.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Unet 3+: A full-scale connected unet for medical image segmenta- tion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:46.543906Z

Source-reported events for the cited work

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Observation f1dd1b82-745c-4260-abbd-4f886d5a4142 · outbound

This paper cites nnu-net: a self-configuring method for deep learning- based biomedical image segmentation.Nature Methods, 18:203–211,.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning nnu-net: a self-configuring method for deep learning- based biomedical image segmentation.Nature Methods, 18:203–211,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:46.306025Z

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Observation d2a0c115-f908-423f-b265-8a9002140902 · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Swin transformer: Hierarchical vision transformer using shifted windows

Reference 14

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Observation 9405623f-ed00-415e-a617-d8a6f8d3dc3a · outbound

This paper cites Distance-based image classification: Generalizing to new classes at near- zero cost.IEEE transactions on pattern analysis and ma- chine intelligence, 35(11):2624–2637,.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Distance-based image classification: Generalizing to new classes at near- zero cost.IEEE transactions on pattern analysis and ma- chine intelligence, 35(11):2624–2637,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:45.802344Z

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Observation ce4a0b02-5155-47f9-9a83-c6a980c28a2d · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning U-net: Convolutional networks for biomedical image segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:45.008578Z

Source-reported events for the cited work

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Observation ca292b06-e0a2-4f11-9aa9-9b170bd5d31f · outbound

This paper cites Meta-learning with memory-augmented neural networks.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Meta-learning with memory-augmented neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:44.701956Z

Source-reported events for the cited work

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Observation f1fa1bca-39ed-4fb2-8ae8-acb937e08226 · outbound

This paper cites Prototypical networks for few-shot learning.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Prototypical networks for few-shot learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:44.147810Z

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Observation ddfb15d7-1cce-491c-98f9-f07ab4e6f976 · outbound

This paper cites Learning to compare: Relation network for few-shot learn- ing.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Learning to compare: Relation network for few-shot learn- ing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:43.787740Z

Source-reported events for the cited work

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Observation abd8056b-8a05-4ab7-8ce9-31b881b32e7c · outbound

This paper cites A shape-based approach to the segmentation of medical imagery using level sets.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning A shape-based approach to the segmentation of medical imagery using level sets

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:43.524613Z

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Observation bada8f09-fd99-4b6e-ae7c-6cca5efd7ff4 · outbound

This paper cites A discriminative feature learning ap- proach for deep face recognition.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning A discriminative feature learning ap- proach for deep face recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:43.096790Z

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Observation ad81e033-4698-41ff-b81a-a7a4d9e815d4 · outbound

This paper cites Weighted res-unet for high-quality retina ves- sel segmentation.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Weighted res-unet for high-quality retina ves- sel segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:42.868928Z

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Observation fcb3a37e-fa1c-404a-8aff-48d3eb87b980 · outbound

This paper cites LinkBERT: Pretraining Language Models with Document Links.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning LinkBERT: Pretraining Language Models with Document Links

Reference 28

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Observation 9134bb32-089d-48cd-a3a9-e5985b390454 · outbound

This paper cites A location- sensitive local prototype network for few-shot medical im- age segmentation.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning A location- sensitive local prototype network for few-shot medical im- age segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:42.575543Z

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Observation d6ef7b6a-f82b-4565-914b-357900eb0f01 · outbound

This paper cites k-means mask transformer.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning k-means mask transformer

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:42.353763Z

Source-reported events for the cited work

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Observation 67cdebb1-e907-40af-b194-c6437ac3f7af · outbound

This paper cites Devil is in the queries: Ad- vancing mask transformers for real-world medical image segmentation and out-of-distribution localization.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Devil is in the queries: Ad- vancing mask transformers for real-world medical image segmentation and out-of-distribution localization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:42.099985Z

Source-reported events for the cited work

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Observation 40a5727e-1418-4d39-8459-16b569b68841 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Unet++: A nested u-net architecture for medical image segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:41.607259Z

Source-reported events for the cited work

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Observation 05867084-a8e8-4e1a-be99-a8efe167095e · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:38.802485Z

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Observation 0e9dec41-b0cb-4771-8757-be18b6e3ca1d · outbound

This paper cites Rethinking semantic segmentation: A prototype view.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Rethinking semantic segmentation: A prototype view

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:41.394315Z

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Observation 820dbd15-fea7-43bf-90b8-5abc71f99e9f · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning nnformer: V olumetric medical image segmen- tation via a 3d transformer.IEEE Transactions on Image Processing, 32:4036–4045, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:41.117775Z

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Observation f09a4662-7f79-4f50-b4f5-37d19f1d1bcd · outbound

This paper cites Matching networks for one shot learning.Advances in neural information pro- cessing systems, 29,.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Matching networks for one shot learning.Advances in neural information pro- cessing systems, 29,

Reference 2003

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:43.322957Z

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Observation c39debba-8c7e-48a7-b5fd-72a3c941be54 · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024,.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024,

Reference 2013

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Observation ded49c46-4faa-4598-8a35-f061e650ed74 · outbound

This paper cites Learning transferable visual models from nat- ural language supervision.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Learning transferable visual models from nat- ural language supervision

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:45.239758Z

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Observation 1c3e7d9c-bffa-4e9e-852f-4ad53d70e8bf · outbound

This paper cites Medical Image Segmentation Using Squeeze-and-Expansion Transformers.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Medical Image Segmentation Using Squeeze-and-Expansion Transformers

Reference 2015

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:42:39.327842Z

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Observation beaa7aeb-6716-4be2-a14e-18e0ce641578 · outbound

This paper cites Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:44.418980Z

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Observation 57816f54-aff5-405a-8c0e-d154e83134ed · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:47.825724Z

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Observation 8b593ecb-cd88-46aa-b676-a39c1f9dd4b1 · outbound

This paper cites Unetr: Trans- formers for 3d medical image segmentation.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Unetr: Trans- formers for 3d medical image segmentation

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:46.802524Z

Source-reported events for the cited work

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Observation 98b1ec2c-b7b5-404d-8e53-c136ffe0c9ec · outbound

This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Schwing, Alexander Kirillov, and Rohit Girdhar

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:47.360065Z

Source-reported events for the cited work

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Observation 852d35ab-6fff-4e31-932b-94b7872a86e1 · outbound

This paper cites A Closer Look at Few-shot Classification.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning A Closer Look at Few-shot Classification

Reference 2020

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Observation 4dced012-41c1-4cdc-84ab-cee1a94bfad1 · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:46.084830Z

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Observation a661dfd4-e952-43a6-8dc0-353383a6aff9 · outbound

This paper cites Semi-Supervised and Active Few-Shot Learning with Prototypical Networks.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Semi-Supervised and Active Few-Shot Learning with Prototypical Networks

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:42:39.673133Z

Source-reported events for the cited work

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Observation 863f83a7-3595-4b77-8c79-94edbc706b5b · outbound

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

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning Dodnet: Learning to segment multi- organ and tumors from multiple partially labeled datasets

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:41.826811Z

Source-reported events for the cited work

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Observation 0f795e77-7f7d-44b7-b8b8-6bc7933b9711 · outbound

This paper cites A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Reference 2024

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Source-reported events for the cited work

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Pith citing papers

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