SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation | Paper Citation Record

Observation 90c5be2f-0fc8-43e8-bbad-0f7b6f59b8d0 · outbound

This paper cites A survey on deep learning in medical image analysis,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation A survey on deep learning in medical image analysis,

Reference 1

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This paper cites A comprehensive survey on deep active learning in medical image analysis.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation A comprehensive survey on deep active learning in medical image analysis

Reference 2

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Observation 70f7f9a7-8a3e-447b-847c-4a8702958ff3 · outbound

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

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation U-net: Convolutional networks for biomedical image segmentation,

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This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

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Observation b3c983c3-e059-42de-a4fe-86cce6ea8e3f · outbound

This paper cites Deep semantic segmentation of natural and medical images: a review,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Deep semantic segmentation of natural and medical images: a review,

Reference 5

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Observation 8d30a4e5-28c2-46dd-8eb0-d35f206955d3 · outbound

This paper cites Density- based one-shot active learning for image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Density- based one-shot active learning for image segmentation,

Reference 6

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This paper cites Cold-start active learning for image classification,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Cold-start active learning for image classification,

Reference 7

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Observation c42fb30d-06f4-409a-8707-c417acffbd07 · outbound

This paper cites Learning to segment medical images with scribble- supervision alone,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Learning to segment medical images with scribble- supervision alone,

Reference 8

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Observation 23f77e2e-905f-4203-9687-4e1f4c205510 · outbound

This paper cites Adversarial learning of object-aware activation map for weakly-supervised semantic segmenta- tion,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Adversarial learning of object-aware activation map for weakly-supervised semantic segmenta- tion,

Reference 9

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Observation 04beb3c0-0924-4acc-8a7f-03395c4668a5 · outbound

This paper cites Attention-based layer fusion and token masking for weakly supervised semantic segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Attention-based layer fusion and token masking for weakly supervised semantic segmentation,

Reference 10

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Observation dc221f8a-9e66-4eb5-852a-01c2d4ba8b70 · outbound

This paper cites A survey on semi-supervised learning,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation A survey on semi-supervised learning,

Reference 11

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Observation 5a3edaea-680e-4ceb-9eb5-40ac47d3e15e · outbound

This paper cites Gct: Graph co-training for semi-supervised few-shot learning,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Gct: Graph co-training for semi-supervised few-shot learning,

Reference 12

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Observation 6f7ad974-f800-4dbc-ab20-58026968e1d6 · outbound

This paper cites Mmatch: Semi- supervised discriminative representation learning for multi-view clas- sification,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Mmatch: Semi- supervised discriminative representation learning for multi-view clas- sification,

Reference 13

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Observation b74b29aa-a1f6-4d19-9b88-c4485e1c68d8 · outbound

This paper cites Mixed-supervised dual-network for medical image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Mixed-supervised dual-network for medical image segmentation,

Reference 14

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Observation 36f63d6c-223a-4a93-ad4c-91c5f58c610b · outbound

This paper cites Label-efficient hybrid- supervised learning for medical image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Label-efficient hybrid- supervised learning for medical image segmentation,

Reference 15

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Observation d50e1859-5db0-41ba-b52c-65d84bcf26d9 · outbound

This paper cites Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images,

Reference 16

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Observation bc0e93eb-888b-4ac1-a791-9799fe33b362 · outbound

This paper cites Teach me to segment with mixed supervision: Confident students become masters,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Teach me to segment with mixed supervision: Confident students become masters,

Reference 17

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Observation 134e40dd-6e5c-41ee-9659-440181fb7fdb · outbound

This paper cites an unresolved cited work.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Unresolved cited work

Reference 18

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Observation 25fcd545-d09a-4642-aef8-36c6a8c1b579 · outbound

This paper cites Learning pixel-level semantic affinity with image- level supervision for weakly supervised semantic segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Learning pixel-level semantic affinity with image- level supervision for weakly supervised semantic segmentation,

Reference 19

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This paper cites Boxsup: Exploiting bounding boxes to super- vise convolutional networks for semantic segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Boxsup: Exploiting bounding boxes to super- vise convolutional networks for semantic segmentation,

Reference 20

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This paper cites What’s the point: Semantic segmentation with point supervision,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation What’s the point: Semantic segmentation with point supervision,

Reference 21

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Observation cd8beae5-6082-4227-9885-23185852da94 · outbound

This paper cites Scribblesup: Scribble- supervised convolutional networks for semantic segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Scribblesup: Scribble- supervised convolutional networks for semantic segmentation,

Reference 22

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Observation a30ce5ba-6bc3-410a-ba6e-8c22667bd5e7 · outbound

This paper cites On regularized losses for weakly-supervised cnn segmen- tation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation On regularized losses for weakly-supervised cnn segmen- tation,

Reference 23

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Observation 2b45f46a-0495-481d-bca5-274acf6f5560 · outbound

This paper cites Scribble-based hierarchical weakly supervised learning for brain tumor segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Scribble-based hierarchical weakly supervised learning for brain tumor segmentation,

Reference 24

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Observation a9eeac0f-a388-4e00-9b21-04610705dc11 · outbound

This paper cites Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency,

Reference 25

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Observation 65c6b5a3-5025-48b0-b7c8-d40ee3624404 · outbound

This paper cites Scribble-supervised medical image segmentation via dual-branch net- work and dynamically mixed pseudo labels supervision,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Scribble-supervised medical image segmentation via dual-branch net- work and dynamically mixed pseudo labels supervision,

Reference 26

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Observation afcf2f9e-b810-4698-9fc4-977dccc18820 · outbound

This paper cites Cyclemix: A holistic strategy for medical image segmentation from scribble supervision,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Cyclemix: A holistic strategy for medical image segmentation from scribble supervision,

Reference 27

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Observation b35432d2-6eb3-4b28-bf39-1b69b9cf549a · outbound

This paper cites Weakly supervised segmentation of covid19 infection with scribble annotation on ct images,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Weakly supervised segmentation of covid19 infection with scribble annotation on ct images,

Reference 28

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This paper cites Learning to segment from scribbles using multi-scale adversarial attention gates,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Learning to segment from scribbles using multi-scale adversarial attention gates,

Reference 29

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This paper cites ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation

Reference 30

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SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Self pseudo entropy knowledge distillation for semi-supervised semantic segmenta- tion,

Reference 31

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This paper cites 3d semi-supervised learning with uncertainty-aware multi- view co-training,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation 3d semi-supervised learning with uncertainty-aware multi- view co-training,

Reference 32

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SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Semi- supervised learning for network-based cardiac mr image segmentation,

Reference 33

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Observation 460524a7-de8f-42da-a3da-54c580566c25 · outbound

This paper cites A two-stream mutual attention network for semi-supervised biomedical segmentation with noisy labels,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation A two-stream mutual attention network for semi-supervised biomedical segmentation with noisy labels,

Reference 34

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SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

Reference 35

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This paper cites Transformation-consistent self-ensembling model for semisupervised medical image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Transformation-consistent self-ensembling model for semisupervised medical image segmentation,

Reference 36

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This paper cites Adversarial dual-student with differentiable spatial warping for semi-supervised semantic segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Adversarial dual-student with differentiable spatial warping for semi-supervised semantic segmentation,

Reference 37

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SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,

Reference 38

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This paper cites Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,

Reference 39

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SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 40

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This paper cites Mutual consistency learning for semi-supervised medical image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Mutual consistency learning for semi-supervised medical image segmentation,

Reference 41

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This paper cites Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,

Reference 42

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Observation d71942b9-436f-4d44-9e6e-6ba3fb8a07c3 · outbound

This paper cites Deep learning with mixed supervision for brain tumor segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Deep learning with mixed supervision for brain tumor segmentation,

Reference 43

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Observation 8dc60812-d5e6-4726-be72-7633d3e49880 · outbound

This paper cites A macro-micro weakly-supervised framework for as-oct tissue segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation A macro-micro weakly-supervised framework for as-oct tissue segmentation,

Reference 44

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Observation f6ec29f7-1d61-4aa9-b25f-6418450f5357 · outbound

This paper cites Semi-supervised semantic segmentation via strong-weak dual-branch network,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Semi-supervised semantic segmentation via strong-weak dual-branch network,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.874495Z

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Observation e340b066-b802-4544-9630-b39fa06d470e · outbound

This paper cites Deep learning based instance segmentation in 3d biomedical images using weak annotation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Deep learning based instance segmentation in 3d biomedical images using weak annotation,

Reference 46

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Observation 797ef551-347e-4110-8914-1bf72b65f8ad · outbound

This paper cites Efficient graph-based image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Efficient graph-based image segmentation,

Reference 47

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Observation 211fd760-62db-451b-8133-a61f67fb823e · outbound

This paper cites Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation,

Reference 48

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Observation ccc860d6-a2cf-4e72-bc9c-87759cac9696 · outbound

This paper cites Chaotic fitness-dependent quasi-reflected aquila optimizer for superpixel based white blood cell segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Chaotic fitness-dependent quasi-reflected aquila optimizer for superpixel based white blood cell segmentation,

Reference 49

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Observation cbbe243f-7128-4cfa-86c9-2415b3ee8f4d · outbound

This paper cites Learning a classification model for segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Learning a classification model for segmentation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.824847Z

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Observation f41b0e3b-68ad-4671-9a15-b0c08b8fba44 · outbound

This paper cites Superpixel image clustering using particle swarm optimizer for nucleus segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Superpixel image clustering using particle swarm optimizer for nucleus segmentation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.813541Z

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Observation bc49c94c-f0f3-4161-96c4-79ffaa11aee6 · outbound

This paper cites A survey on the utilization of superpixel image for clustering based image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation A survey on the utilization of superpixel image for clustering based image segmentation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.802008Z

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Observation cc0f1320-d094-4d4f-9a88-a6e6e6f04f70 · outbound

This paper cites Self- supervision with superpixels: Training few-shot medical image seg- mentation without annotation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Self- supervision with superpixels: Training few-shot medical image seg- mentation without annotation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.790190Z

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Observation 3d7cc3bf-25e5-4247-8d24-0eb1951f64f8 · outbound

This paper cites Separated contrastive learning for organ-at-risk and gross-tumor-volume segmen- tation with limited annotation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Separated contrastive learning for organ-at-risk and gross-tumor-volume segmen- tation with limited annotation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.778833Z

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Observation ea9e7092-2bab-4b56-9b6e-a1394543c456 · outbound

This paper cites Pseudo-label refine- ment using superpixels for semi-supervised brain tumour segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Pseudo-label refine- ment using superpixels for semi-supervised brain tumour segmentation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.766812Z

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Observation ccdfce17-76f8-40dc-8fd6-f8e67cab4506 · outbound

This paper cites Superpixel-guided iterative learning from noisy labels for medical image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Superpixel-guided iterative learning from noisy labels for medical image segmentation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.754923Z

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Observation 1e01f5d8-16bd-4cf6-87bf-9fadf0cf504b · outbound

This paper cites FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Reference 57

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Observation 70fb8129-2bde-4483-a9f7-927da1cd157d · outbound

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

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T18:22:31.503469Z

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Observation 3d2b3b33-f20d-4517-8d6e-dce11de5bf25 · outbound

This paper cites Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:22:31.593705Z

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Observation 298567a3-c524-49ac-b035-90f1e29712d7 · outbound

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

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation The multimodal brain tumor image segmentation benchmark (brats),

Reference 60

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Observation d25ae632-cdec-43c3-b57d-f67bb18eb83c · outbound

This paper cites WSL4MIS,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation WSL4MIS,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.728983Z

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Observation e30292aa-6fb4-447a-a659-d1ce1e956efe · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Pytorch: An imperative style, high-performance deep learning library,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T18:22:31.517432Z

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Observation 96916705-cfc7-4af7-b18a-c77c6cefc6b1 · outbound

This paper cites Interpolation consistency training for semi-supervised learning,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Interpolation consistency training for semi-supervised learning,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.711394Z

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Observation 5ca73175-e634-4228-afcb-63532153bc38 · outbound

This paper cites On regularized losses for weakly-supervised cnn segmen- tation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation On regularized losses for weakly-supervised cnn segmen- tation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.699828Z

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Observation 9c3cfaff-5508-46f9-840b-7666bfb8fe9d · outbound

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

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Slic superpixels compared to state-of-the-art,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.688991Z

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Observation 314dd658-ccd0-4598-ae1b-179e18b05e08 · outbound

This paper cites Transferring and regularizing prediction for semantic segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Transferring and regularizing prediction for semantic segmentation,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.678361Z

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

Observation d91f6c5f-f943-46b6-9ad1-886557637587 · outbound

This paper cites Self- supervised learning for few-shot medical image segmentation,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Self- supervised learning for few-shot medical image segmentation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.667782Z

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Observation 4395e078-f00b-4f1f-b125-f8e4e4934aa9 · outbound

This paper cites Supra: Superpixel guided loss for improved multi-modal segmentation in endoscopy,.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Supra: Superpixel guided loss for improved multi-modal segmentation in endoscopy,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:22:31.657073Z

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Observation b0e52829-d519-484e-b491-d810ee9f13d5 · outbound

This paper cites Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation.

SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:22:31.577495Z

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