Paper Citation Record · LEDGER

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training

As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2504.14737.

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

pith.paper-citation-record.v1
2504.14737 v1

Coverage vector

measured 46 of 46 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-05-11T08:40:58.287721Z

Reference resolution

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External citation measurements

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Outbound references

Observation 3603348f-a842-41b5-b196-425263f8cb15 · outbound

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

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training A simple framework for contrastive learning of visual representations,

Reference 1

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Observation c05e0784-5609-4b0d-8d1b-11f990ea3716 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Momentum contrast for unsupervised visual representation learning,

Reference 2

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Observation aa4efdf9-6221-4115-8da5-477fad82366d · outbound

This paper cites Contrastive learning of global and local features for medical image segmentation with limited annotations,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Contrastive learning of global and local features for medical image segmentation with limited annotations,

Reference 3

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Observation e82b9021-0136-4e95-95f8-080d3bfd6ba2 · outbound

This paper cites Positional contrastive learning for volumetric medical image segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Positional contrastive learning for volumetric medical image segmentation,

Reference 4

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Observation 1f325ec1-20a5-45df-8df8-747c82842f55 · outbound

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

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis,

Reference 5

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Observation 870d4d01-4849-480e-99f0-2440181d869d · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assign- ments,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Unsupervised learning of visual features by contrasting cluster assign- ments,

Reference 6

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Observation ebcb3464-2b5a-4d9d-b9f6-6dae51c51e2d · outbound

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

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Bootstrap your own latent-a new approach to self-supervised learning,

Reference 7

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Observation 21153cd6-e1f0-4973-a618-1c20dd3250c2 · outbound

This paper cites Exploring simple siamese representation learning,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Exploring simple siamese representation learning,

Reference 8

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Observation e64ac4a0-a7ed-4bc0-a12f-adb58a260e64 · outbound

This paper cites Weakly supervised contrastive learning,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Weakly supervised contrastive learning,

Reference 9

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Observation facc9efd-691d-48e3-a7d0-b809cf4daee1 · outbound

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

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Desd: Self-supervised learning with deep self-distillation for 3d medical image segmentation,

Reference 10

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Observation 68a43d3e-7349-4f4d-b4b8-c7b3516221c9 · outbound

This paper cites Simtriplet: Simple triplet representation learning with a single gpu,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Simtriplet: Simple triplet representation learning with a single gpu,

Reference 11

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Observation d26d97e6-ed12-4e29-9683-5101bf1f3ba4 · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Slic superpixels compared to state-of-the-art superpixel methods,

Reference 12

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Observation b0e5fce9-9a64-46c2-8d89-6939e2bff4e8 · outbound

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

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training U-net: Convolutional networks for biomedical image segmentation,

Reference 13

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Observation 545cbf5b-f6e3-49e5-b0f3-c21ebfec4132 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Attention U-Net: Learning Where to Look for the Pancreas

Reference 14

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Observation 2fd2d577-d95f-4925-b407-05629e41bfe1 · outbound

This paper cites Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with trans- former,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with trans- former,

Reference 15

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Observation 7c413261-12dc-49d3-ad37-a432c821dd1e · outbound

This paper cites Bi- directional convlstm u-net with densley connected convolutions,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Bi- directional convlstm u-net with densley connected convolutions,

Reference 16

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Observation c515df99-c2ba-4382-acf5-39d2274ed3ac · outbound

This paper cites The importance of skip connections in biomedical image segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training The importance of skip connections in biomedical image segmentation,

Reference 17

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Observation a071bc8a-de06-4cd0-bfad-635b5ce2cfb4 · outbound

This paper cites Rolling-unet: Re- vitalizing mlp’s ability to efficiently extract long-distance dependencies for medical image segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Rolling-unet: Re- vitalizing mlp’s ability to efficiently extract long-distance dependencies for medical image segmentation,

Reference 18

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Observation 77863f57-c9c1-45d5-a47b-2904456ceec6 · outbound

This paper cites U-kan makes strong backbone for medical image segmentation and generation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training U-kan makes strong backbone for medical image segmentation and generation,

Reference 19

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Observation 9531a49b-c732-4d7d-bd26-2b4ce4c94576 · outbound

This paper cites Superpixels and polygons using simple non-iterative clustering,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Superpixels and polygons using simple non-iterative clustering,

Reference 20

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Observation 61614263-59f8-4814-be85-3925e523761d · outbound

This paper cites Superpixel segmentation using linear spectral clustering,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Superpixel segmentation using linear spectral clustering,

Reference 21

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Observation 625d965d-b622-4a19-b6fa-6b7b8322de92 · outbound

This paper cites Efficient Graph-Based image segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Efficient Graph-Based image segmentation,

Reference 22

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Observation 2be7ca17-4af4-4fa8-b10d-042c03132bf1 · outbound

This paper cites Entropy rate superpixel segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Entropy rate superpixel segmentation,

Reference 23

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Observation eae2b8ef-b454-4ea3-8ffa-9c337020ac7f · outbound

This paper cites Learning superpixels with segmentation-aware affinity loss,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Learning superpixels with segmentation-aware affinity loss,

Reference 24

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Observation 942a4988-3b82-4d5c-b1ec-3ed8c09e6a18 · outbound

This paper cites Superpixel sampling networks,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Superpixel sampling networks,

Reference 25

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Observation 9ce9e5ac-286e-4118-867b-a1060d5c0a40 · outbound

This paper cites Superpixel segmentation with fully convolutional networks,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Superpixel segmentation with fully convolutional networks,

Reference 26

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This paper cites Learning the superpixel in a non-iterative and lifelong manner,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Learning the superpixel in a non-iterative and lifelong manner,

Reference 27

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Observation f59ee0c4-922e-4684-94c5-352b55b2af33 · outbound

This paper cites Whole heart and great vessel segmentation in congenital heart disease using deep neural networks and graph matching,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Whole heart and great vessel segmentation in congenital heart disease using deep neural networks and graph matching,

Reference 28

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Observation dd6f960e-4674-4cd8-893a-2e8db0b367f6 · outbound

This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 29

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

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge,

Reference 30

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Observation ea2f8e29-009a-4dc9-b866-647c49bb8a56 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?

Reference 31

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Observation bb5b77ce-0cdb-4e32-8a9d-6f22d9ff8f55 · outbound

This paper cites Challenges and methodologies of fully automatic whole heart segmentation: a review,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Challenges and methodologies of fully automatic whole heart segmentation: a review,

Reference 32

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Observation 8728872d-d542-4bb3-8f1b-ff1c1c24a527 · outbound

This paper cites Interactive whole-heart segmentation in congenital heart disease,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Interactive whole-heart segmentation in congenital heart disease,

Reference 33

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This paper cites CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation,

Reference 34

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This paper cites The medical segmentation decathlon,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training The medical segmentation decathlon,

Reference 35

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This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 36

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This paper cites Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

Reference 37

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Observation f330a7d3-553c-4118-9838-dff418f34f40 · outbound

This paper cites Caussl: Causality- inspired semi-supervised learning for medical image segmentation,.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Caussl: Causality- inspired semi-supervised learning for medical image segmentation,

Reference 38

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Observation 986f6649-af88-4b94-bae3-66298ac492ea · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 39

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This paper cites Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

Reference 40

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Observation 8a1181ac-7f46-476f-a1dd-197804a1929c · outbound

This paper cites Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

Reference 41

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Observation d6e7df47-fba6-49c8-bb47-37d7332797bc · outbound

This paper cites Semi-supervised Medical Image Segmentation through Dual-task Consistency.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Semi-supervised Medical Image Segmentation through Dual-task Consistency

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:45:42.982774Z

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Observation 52ec0311-d68c-4070-a7be-28a775b61835 · outbound

This paper cites Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency

Reference 43

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This paper cites Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Reference 44

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Observation 86fc841b-9afe-4bf3-9d16-aff1b7e96118 · outbound

This paper cites Mutual Consistency Learning for Semi-supervised Medical Image Segmentation.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:45:42.911451Z

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Observation 81ce675b-2eb2-465c-a89d-25e54acd36cf · outbound

This paper cites Similar to the encoder, the decoder of UNet also consists of 4 convolutional blocks and a projection head.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Similar to the encoder, the decoder of UNet also consists of 4 convolutional blocks and a projection head

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:43.145722Z

Source-reported events for the cited work

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Pith citing papers

Observation e8706a46-490c-4df5-b36f-2ca43835cd47 · inbound

RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation cites this paper.

RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training

Reference 6

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