Paper Citation Record · LEDGER

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation

As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2502.02489.

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

pith.paper-citation-record.v1
2502.02489 v1

Coverage vector

measured 40 of 40 reference resolution

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measured 41 of 41 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:36:42.073968Z

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

Source: pith, observed 2026-08-06T14:36:42.264224Z

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation d648311b-352d-4d89-b03d-15ebec608724 · outbound

This paper cites Application of ultrasound in medicine,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Application of ultrasound in medicine,

Reference 1

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Observation 809249f7-58ae-4a69-9141-3c93aa00836d · outbound

This paper cites Machine learning for medical ultrasound: status, methods, and future opportunities,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Machine learning for medical ultrasound: status, methods, and future opportunities,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:02:59.511281Z

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Observation 44336597-bdb6-487d-a8ed-4a0ac39d8dcd · outbound

This paper cites A hybrid enhanced attention transformer network for medical ultrasound image segmentation,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation A hybrid enhanced attention transformer network for medical ultrasound image segmentation,

Reference 3

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Observation ecf20477-51a4-49d7-b5d8-d7ed23f8ec18 · outbound

This paper cites HAU-Net: Hybrid CNN-transformer for breast ultra- sound image segmentation,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation HAU-Net: Hybrid CNN-transformer for breast ultra- sound image segmentation,

Reference 4

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Observation 53cc0692-3c32-4f60-8dee-1d1c396cdf37 · outbound

This paper cites Cross-Image Dependency Modeling for Breast Ultrasound Segmentation,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Cross-Image Dependency Modeling for Breast Ultrasound Segmentation,

Reference 5

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Observation 762577c2-f949-4c60-b647-37540fef9b6e · outbound

This paper cites Unified semantic model for medical image segmenta- tion,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Unified semantic model for medical image segmenta- tion,

Reference 6

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Observation 67e6c856-f454-4088-baf1-1abbe34599d3 · outbound

This paper cites Self-supervised learning is more robust to dataset imbalance,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Self-supervised learning is more robust to dataset imbalance,

Reference 7

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Observation 6340f882-1830-4e7c-988b-1cad7cafe66c · outbound

This paper cites Toward Generalizability in the Deployment of Artifi- cial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Toward Generalizability in the Deployment of Artifi- cial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification,

Reference 8

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Observation e805bff7-fc51-405a-b2b5-76894f26b020 · outbound

This paper cites Big Self-Supervised Models Advance Medical Image Classification,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Big Self-Supervised Models Advance Medical Image Classification,

Reference 9

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Observation 735752cd-4e69-4de8-aaf0-40454d540e19 · outbound

This paper cites Self-supervised learning for medical image analysis using image context restoration,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Self-supervised learning for medical image analysis using image context restoration,

Reference 10

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Observation 2d3cd481-6d0b-4dc5-ad39-71e9098508c8 · outbound

This paper cites SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis,

Reference 11

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Observation 9ef9b677-b240-48c0-8319-d1fc58111186 · outbound

This paper cites VanBerlo et al., “A survey of the impact of self-supervised pretraining 12 Fig.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation VanBerlo et al., “A survey of the impact of self-supervised pretraining 12 Fig

Reference 12

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Observation 1a1d8a0d-5f0a-44ef-a2bb-5c7c19544144 · outbound

This paper cites Self-Supervised Learning to More Efficiently Generate Segmentation Masks for Wrist Ultrasound,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Self-Supervised Learning to More Efficiently Generate Segmentation Masks for Wrist Ultrasound,

Reference 13

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Observation 0a14856d-2935-433c-909b-208f9d6c28fb · outbound

This paper cites Thyroid ultrasound diagnosis improvement via multi- view self-supervised learning and two-stage pre-training,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Thyroid ultrasound diagnosis improvement via multi- view self-supervised learning and two-stage pre-training,

Reference 14

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Observation 21e00ae5-ea36-423d-9441-d2f0b899433c · outbound

This paper cites Self-Supervised Learning: Generative or Contrastive,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Self-Supervised Learning: Generative or Contrastive,

Reference 15

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Observation 87274d0a-baf3-4052-85a7-b354dc1342ea · outbound

This paper cites Self-Supervised Learning of Pretext- Invariant Representations,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Self-Supervised Learning of Pretext- Invariant Representations,

Reference 16

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Observation e79c7424-6ac1-4dc8-9cdb-456504bb8df9 · outbound

This paper cites Unsupervised Learning of Visual Represen- tations by Solving Jigsaw Puzzles,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Unsupervised Learning of Visual Represen- tations by Solving Jigsaw Puzzles,

Reference 17

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Observation a36350d8-b29e-40b8-b266-517f62b7d8df · outbound

This paper cites A simple framework for contrastive learning of visual representations.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation A simple framework for contrastive learning of visual representations

Reference 18

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This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 19

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Observation 2735f2e3-cc07-4d7f-90f0-96db69718402 · outbound

This paper cites ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation,

Reference 20

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Observation e8e4be40-1ca7-482a-996a-d1de81e38c44 · outbound

This paper cites Ultrasound spine image segmentation using multi-scale feature fusion skip-inception U-Net (SIU-Net),.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Ultrasound spine image segmentation using multi-scale feature fusion skip-inception U-Net (SIU-Net),

Reference 21

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Observation a166bc3f-3caa-4ef4-b5a4-895d396f031d · outbound

This paper cites Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture,

Reference 22

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This paper cites Dilated Squeeze-and-Excitation U-Net for Fetal Ultrasound Image Segmentation,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Dilated Squeeze-and-Excitation U-Net for Fetal Ultrasound Image Segmentation,

Reference 23

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This paper cites Unsupervised representation learning by predicting image rotations,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Unsupervised representation learning by predicting image rotations,

Reference 24

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Observation 52bfbbc7-3daf-43b9-91d6-47e8d738ce4e · outbound

This paper cites Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning frame- work,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning frame- work,

Reference 25

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Observation e6c10f30-6536-4da6-947f-acfc633b56c4 · outbound

This paper cites Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images,

Reference 26

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Observation 5ec7bebc-19ef-439b-abbc-90561525b00c · outbound

This paper cites SimMIM: a Simple Framework for Masked Image Modeling,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation SimMIM: a Simple Framework for Masked Image Modeling,

Reference 27

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Observation f845cca3-1088-4921-96e1-cd243617b64e · outbound

This paper cites Momentum Contrast for Unsupervised Visual Represen- tation Learning,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Momentum Contrast for Unsupervised Visual Represen- tation Learning,

Reference 28

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Observation 20ceed1b-f269-4623-8735-9fa37da9fa6a · outbound

This paper cites Bootstrap your own latent-a new approach to self- supervised learning,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Bootstrap your own latent-a new approach to self- supervised learning,

Reference 29

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Observation f31eb849-a867-4b01-a2c0-cf819ed33541 · outbound

This paper cites Siamese neural networks for one-shot image recogni- tion,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Siamese neural networks for one-shot image recogni- tion,

Reference 30

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Observation 2a34a4ff-682e-49e6-be9e-c501aee449ac · outbound

This paper cites Prototypical networks for few-shot learning,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Prototypical networks for few-shot learning,

Reference 31

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Observation 0cb15115-5da0-43aa-b573-0132954616c6 · outbound

This paper cites Learning to Compare: Relation Network for Few-Shot Learning,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Learning to Compare: Relation Network for Few-Shot Learning,

Reference 32

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Observation 69a2063f-0500-41c5-92a1-563c52be814b · outbound

This paper cites ImageNet: A large-scale hierarchical image database,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation ImageNet: A large-scale hierarchical image database,

Reference 33

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Observation 0af6cb1c-e855-42e5-af1d-8a8382c1092e · outbound

This paper cites Deep Residual Learning for Image Recognition,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Deep Residual Learning for Image Recognition,

Reference 34

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Observation 2d377dea-ba71-4f3d-bca0-00a550b2afb6 · outbound

This paper cites Unsupervised Feature Learning via Non-parametric Instance Discrimination,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Unsupervised Feature Learning via Non-parametric Instance Discrimination,

Reference 35

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Observation f08158fd-5269-4e5f-bf4b-3d729cc0217a · outbound

This paper cites Self-Supervised Learning for Accurate Liver View Classification in Ultrasound Images with Minimal Labeled Data,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Self-Supervised Learning for Accurate Liver View Classification in Ultrasound Images with Minimal Labeled Data,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:02:59.181699Z

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Observation 60c2abfa-c8c7-4cc7-9d92-8367c1ad7e8a · outbound

This paper cites Dataset of breast ultrasound images,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Dataset of breast ultrasound images,

Reference 37

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Observation 690b2981-a421-40fc-bcd4-c476d1bca732 · outbound

This paper cites Curated benchmark dataset for ultrasound based breast lesion analysis,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Curated benchmark dataset for ultrasound based breast lesion analysis,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:02:59.160443Z

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This paper cites Automated breast ultrasound lesions detection using convolutional neural networks,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Automated breast ultrasound lesions detection using convolutional neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:02:59.149930Z

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Observation d15aff1e-0b08-45a5-9388-1170ffe539bc · outbound

This paper cites Road extraction by deep Residual U-Net,.

A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation Road extraction by deep Residual U-Net,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:02:59.138964Z

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Pith citing papers

Observation 28ec5300-0345-4121-a455-9f5f35237429 · inbound

Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss cites this paper.

Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation

Reference 7

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Source-reported events for the cited work

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