Paper Citation Record · LEDGER

ShapeEmbed: a self-supervised learning framework for 2D contour quantification

As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2507.01009.

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

pith.paper-citation-record.v1
2507.01009 v1

Coverage vector

measured 62 of 62 reference resolution

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measured 64 of 64 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:27:26.841134Z

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

Source: pith, observed 2026-08-05T17:27:26.952007Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 95dde69d-8e7d-4c2b-8074-a25e9f8666ea · outbound

This paper cites Identification of everyday objects on the basis of silhouette and outline versions.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Identification of everyday objects on the basis of silhouette and outline versions

Reference 1

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Observation ebc2b33d-19b8-455d-8db9-961aff0742c5 · outbound

This paper cites Statistical shape analysis: with applications in R, volume 995.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Statistical shape analysis: with applications in R, volume 995

Reference 2

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Observation b6bd5597-0184-4847-a92d-4892b13c62d2 · outbound

This paper cites Biology and physics of cell shape changes in development.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Biology and physics of cell shape changes in development

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.913524Z

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Observation f8069e5d-4f0f-4766-a903-1e15a2606d3c · outbound

This paper cites Decoding information in cell shape.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Decoding information in cell shape

Reference 4

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Observation 9752f1ce-9243-47d4-b90d-bdaa65efc042 · outbound

This paper cites Cell and nucleus shape as an indicator of tissue fluidity in carcinoma.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Cell and nucleus shape as an indicator of tissue fluidity in carcinoma

Reference 5

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Observation 05463ac2-7029-4a51-a97f-0c0990df92e4 · outbound

This paper cites Morphofeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Morphofeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.856415Z

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Observation 6dd3e1eb-55bf-4f5d-b090-0c26025c76d3 · outbound

This paper cites Image-based multivariate profiling of drug responses from single cells.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Image-based multivariate profiling of drug responses from single cells

Reference 7

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Observation ffccf935-3db9-4bd4-a882-b8188239064b · outbound

This paper cites Visualizing cellular imaging data using phenoplot.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Visualizing cellular imaging data using phenoplot

Reference 8

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Observation 2fab1cc0-23e0-4b53-9ffa-65f5b6ca433e · outbound

This paper cites Comparison of quantitative methods for cell-shape analysis.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Comparison of quantitative methods for cell-shape analysis

Reference 9

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Observation 06fdff75-4752-4bdf-baa3-3e391da8792d · outbound

This paper cites Reducing the dimensionality of data with neural networks.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Reducing the dimensionality of data with neural networks

Reference 10

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Observation 67a13633-2f58-403e-896e-8f6faa3b429e · outbound

This paper cites Auto-Encoding Variational Bayes.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Auto-Encoding Variational Bayes

Reference 11

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Observation eb554706-958d-4706-99a9-24d16e6500ab · outbound

This paper cites Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

Reference 12

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Observation 028358db-ee03-4b17-8445-8688d65fa8f3 · outbound

This paper cites Evaluation of methods for generative modeling of cell and nuclear shape.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Evaluation of methods for generative modeling of cell and nuclear shape

Reference 13

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Observation 88cbf116-7947-4e33-804b-f15b296f6333 · outbound

This paper cites Kendall shape-vae: Learning shapes in a generative framework.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Kendall shape-vae: Learning shapes in a generative framework

Reference 14

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Observation 23c8f0d2-b44a-4b03-821c-76b411f43e87 · outbound

This paper cites Continuous kendall shape variational autoencoders.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Continuous kendall shape variational autoencoders

Reference 15

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Observation 38c89b2a-b912-4fdd-b6f9-8092719e619d · outbound

This paper cites Euclidean distance matrices: essential theory, algorithms, and applications.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Euclidean distance matrices: essential theory, algorithms, and applications

Reference 16

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Observation 45310871-251a-4789-a885-4890fe7741fc · outbound

This paper cites Multidimensional scaling.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Multidimensional scaling

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.667809Z

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Observation d067d0af-2e6c-4a14-be58-a60aec0b5a31 · outbound

This paper cites Multiscale distance matrix for fast plant leaf recognition.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Multiscale distance matrix for fast plant leaf recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.650746Z

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Observation a5120298-4cf0-4002-85e9-11fe28af338e · outbound

This paper cites Wesd--weighted spectral distance for measuring shape dissimilarity.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Wesd--weighted spectral distance for measuring shape dissimilarity

Reference 19

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This paper cites Cajal enables analysis and integration of single-cell morphological data using metric geometry.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Cajal enables analysis and integration of single-cell morphological data using metric geometry

Reference 20

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Observation 689ee5df-21c1-49ce-b436-f501d7325cb5 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Highly accurate protein structure prediction with alphafold

Reference 21

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This paper cites S ch\"onberger, J uan Nunez-Iglesias , F ran c ois B oulogne, J oshua D.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification S ch\"onberger, J uan Nunez-Iglesias , F ran c ois B oulogne, J oshua D

Reference 22

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This paper cites Quantitative morphological signatures define local signaling networks regulating cell morphology.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Quantitative morphological signatures define local signaling networks regulating cell morphology

Reference 23

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This paper cites Identification of phenotype-specific networks from paired gene expression--cell shape imaging data.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Identification of phenotype-specific networks from paired gene expression--cell shape imaging data

Reference 24

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This paper cites Shape discrimination using fourier descriptors.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Shape discrimination using fourier descriptors

Reference 25

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Observation 25a1a7bd-3cf8-4b54-b584-714e61897bf0 · outbound

This paper cites Elliptic fourier features of a closed contour.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Elliptic fourier features of a closed contour

Reference 26

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

ShapeEmbed: a self-supervised learning framework for 2D contour quantification A simple framework for contrastive learning of visual representations

Reference 27

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This paper cites Momentum contrast for unsupervised visual representation learning.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Momentum contrast for unsupervised visual representation learning

Reference 28

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This paper cites Emerging properties in self-supervised vision transformers.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Emerging properties in self-supervised vision transformers

Reference 29

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This paper cites Masked autoencoders are scalable vision learners.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Masked autoencoders are scalable vision learners

Reference 30

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This paper cites Jamieson, Erik S.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Jamieson, Erik S

Reference 31

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This paper cites Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles

Reference 32

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This paper cites Invariant Shape Representation Learning For Image Classification.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Invariant Shape Representation Learning For Image Classification

Reference 33

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This paper cites A rotation-invariant framework for deep point cloud analysis.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification A rotation-invariant framework for deep point cloud analysis

Reference 34

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This paper cites Riconv++: Effective rotation invariant convolutions for 3d point clouds deep learning.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Riconv++: Effective rotation invariant convolutions for 3d point clouds deep learning

Reference 35

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification A closer look at rotation-invariant deep point cloud analysis

Reference 36

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Observation dd9f5559-a921-4602-8515-b93e1ffc73d4 · outbound

This paper cites Ri-mae: Rotation-invariant masked autoencoders for self-supervised point cloud representation learning.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Ri-mae: Rotation-invariant masked autoencoders for self-supervised point cloud representation learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.719117Z

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification Self-supervised learning of rotation-invariant 3d point set features using transformer and its self-distillation

Reference 38

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification General $E(2)$-Equivariant Steerable CNNs

Reference 39

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification A high resolution 3d surface construction algorithm

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.456242Z

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Observation f08a66f4-14d0-44dc-be7c-60ad35c670bc · outbound

This paper cites Representation learning: A review and new perspectives.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Representation learning: A review and new perspectives

Reference 41

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification Deep residual learning for image recognition

Reference 42

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This paper cites How shift equivariance impacts metric learning for instance segmentation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification How shift equivariance impacts metric learning for instance segmentation

Reference 43

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This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.126550Z

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Observation c0c7f850-be39-49f2-adcb-afe451fe4873 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification The mnist database of handwritten digit images for machine learning research

Reference 45

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification B enchmarking image database for shape recognition techniques

Reference 46

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This paper cites Annotated high-throughput microscopy image sets for validation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Annotated high-throughput microscopy image sets for validation

Reference 47

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This paper cites Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz, and Pei-Hsun Wu.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz, and Pei-Hsun Wu

Reference 48

Resolution: verified exact; doi, observed 2026-08-06T21:06:11.166984Z

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Observation d79c5557-b7b9-49b9-b9c6-704ee04fe9e4 · outbound

This paper cites Logistic Regression, pages 243--250.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Logistic Regression, pages 243--250

Reference 49

Resolution: verified exact; doi, observed 2026-08-06T21:06:11.149945Z

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Observation 95c393b5-e439-4824-86c6-5546119e83f5 · outbound

This paper cites Chai, Wee Sun Lee, and Hai Leong Chieu.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Chai, Wee Sun Lee, and Hai Leong Chieu

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.875393Z

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Observation 49b29ab2-3f5c-44a5-b06a-ddb9188d1508 · outbound

This paper cites Shape distributions.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Shape distributions

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.784548Z

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Observation b3ccfba2-38a0-4ca4-a06d-6810adb724e8 · outbound

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ShapeEmbed: a self-supervised learning framework for 2D contour quantification Pytorch: An imperative style, high-performance deep learning library

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.680609Z

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Reference 57

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Reference 62

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Pith citing papers

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Reference 6

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