Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation | Paper Citation Record

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This paper cites Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank

Reference 1

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This paper cites Pseudo-label guided contrastive learning for semi-supervised medical image segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Pseudo-label guided contrastive learning for semi-supervised medical image segmentation

Reference 2

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Observation f35d49e5-cae1-41e7-81b8-87f9d61118c0 · outbound

This paper cites Address- ing class imbalance in semi-supervised image segmenta- tion: A study on cardiac mri.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Address- ing class imbalance in semi-supervised image segmenta- tion: A study on cardiac mri

Reference 3

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This paper cites Rep- resentation and fusion of heterogeneous fuzzy informa- tion in the 3d space for model-based structural recogni- tion—application to 3d brain imaging.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Rep- resentation and fusion of heterogeneous fuzzy informa- tion in the 3d space for model-based structural recogni- tion—application to 3d brain imaging

Reference 4

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Observation c9ad437c-a0bf-4000-b222-d932b1e5fba7 · outbound

This paper cites What is the effect of importance weighting in deep learning? In Proc.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation What is the effect of importance weighting in deep learning? In Proc

Reference 5

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Observation dcc9b01a-1aaa-4cd1-bd96-792635438644 · outbound

This paper cites Orthogonal annotation benefits barely- supervised medical image segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Orthogonal annotation benefits barely- supervised medical image segmentation

Reference 6

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Observation f7549be9-978b-41f0-8e67-023296b8d610 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Learning imbalanced datasets with label-distribution-aware margin loss

Reference 7

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Observation 90c521b7-25e6-4991-8866-1e97ac34f6c6 · outbound

This paper cites Magicnet: Semi-supervised multi- organ segmentation via magic-cube partition and recov- ery.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Magicnet: Semi-supervised multi- organ segmentation via magic-cube partition and recov- ery

Reference 8

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Observation 69c59aa7-3300-43fc-b2f6-848fd60a0d7a · outbound

This paper cites An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning

Reference 9

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Observation e7fea131-1e02-44c8-8ca6-c4382aa486b6 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Semi-supervised semantic segmentation with cross pseudo supervision

Reference 10

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Observation 47d11521-3458-4f91-8a2f-7aa3327b2f73 · outbound

This paper cites Semi-supervised and unsupervised deep visual learning: A survey.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Semi-supervised and unsupervised deep visual learning: A survey

Reference 11

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Observation db7c8266-fa17-4afd-bf3d-229bc6adb5e9 · outbound

This paper cites Shape transforma- tion driven by active contour for class-imbalanced semi- supervised medical image segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Shape transforma- tion driven by active contour for class-imbalanced semi- supervised medical image segmentation

Reference 12

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Observation 64a8920b-8603-4d0d-b13f-bbb8172eac95 · outbound

This paper cites Dual structure- aware image filterings for semi-supervised medical im- age segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Dual structure- aware image filterings for semi-supervised medical im- age segmentation

Reference 13

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Observation 482dfb32-675b-4f90-86dc-5f1a7760dcfd · outbound

This paper cites Class-imbalanced semi- supervised learning with adaptive thresholding.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Class-imbalanced semi- supervised learning with adaptive thresholding

Reference 14

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Observation 3ed6ec53-8545-4563-aa4b-24084ad23781 · outbound

This paper cites Learning topo- logical interactions for multi-class medical image seg- mentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Learning topo- logical interactions for multi-class medical image seg- mentation

Reference 15

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Observation cebc50ed-45a7-4a4f-ba39-b3b409f74c4c · outbound

This paper cites Topology-aware uncertainty for image segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Topology-aware uncertainty for image segmentation

Reference 16

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Observation 246b7c30-15d7-4ab0-978f-df03c646cba8 · outbound

This paper cites Cat: Coordinat- ing anatomical-textual prompts for multi-organ and tu- mor segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Cat: Coordinat- ing anatomical-textual prompts for multi-organ and tu- mor segmentation

Reference 17

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Observation aa63bc8a-b5cb-4fe1-a1c8-0e51b19cf187 · outbound

This paper cites AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

Reference 18

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Observation e19b2ceb-bb54-4a13-89c1-359b5dd2195a · outbound

This paper cites Zept: Zero-shot pan- tumor segmentation via query-disentangling and self- prompting.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Zept: Zero-shot pan- tumor segmentation via query-disentangling and self- prompting

Reference 19

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Observation de5f1741-a1bf-4b8a-821c-c20fc803351a · outbound

This paper cites UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities

Reference 20

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Observation 02e2d0fc-9254-4e55-bbaf-b4782d086eb0 · outbound

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

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Miccai multi-atlas labeling be- yond the cranial vault–workshop and challenge

Reference 21

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Observation b9f03b36-5ba0-4b1b-97b7-40174f4dca30 · outbound

This paper cites Grounded language- image pre-training.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Grounded language- image pre-training

Reference 22

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Observation 4da54956-b5c1-4d5c-962e-d59dedc3b8f6 · outbound

This paper cites Shape-aware semi-supervised 3d semantic segmentation for medical images.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Shape-aware semi-supervised 3d semantic segmentation for medical images

Reference 23

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Observation 8be5f75d-927c-45ca-8040-5d3f5ee5c32b · outbound

This paper cites Pmc-clip: Contrastive language-image pre-training using biomedi- cal documents.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Pmc-clip: Contrastive language-image pre-training using biomedi- cal documents

Reference 24

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Observation 52f64945-6c93-4cdd-80bb-4a5749505b14 · outbound

This paper cites Calibrating label distribution for class- imbalanced barely-supervised knee segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Calibrating label distribution for class- imbalanced barely-supervised knee segmentation

Reference 25

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Observation 174e1f55-c00b-4971-ba29-b50479054ea5 · outbound

This paper cites Visual instruction tuning.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Visual instruction tuning

Reference 26

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Observation f0c1d058-0d6a-4d8b-80e9-32df8e894f6e · outbound

This paper cites Clip-driven universal model for organ segmentation and tumor de- tection.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Clip-driven universal model for organ segmentation and tumor de- tection

Reference 27

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Observation 77312b9f-0394-4115-8647-09619c0cd394 · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Large-scale long-tailed recognition in an open world

Reference 28

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Observation 14b2feaa-8d92-4131-af2f-87373d8aee1e · outbound

This paper cites Semi-supervised medical image segmentation through dual-task consistency.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Semi-supervised medical image segmentation through dual-task consistency

Reference 29

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Observation 6934aedf-5723-4dac-bee8-8539b4bc6211 · outbound

This paper cites Pseudo- label guided image synthesis for semi-supervised covid- 19 pneumonia infection segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Pseudo- label guided image synthesis for semi-supervised covid- 19 pneumonia infection segmentation

Reference 30

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Observation 60f604bb-9315-4956-b7b3-5516d949a6bd · outbound

This paper cites Uncertainty-guided dual- views for semi-supervised volumetric medical image segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Uncertainty-guided dual- views for semi-supervised volumetric medical image segmentation

Reference 31

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Observation 50907711-0dd9-4190-b46f-ac97880b1c0d · outbound

This paper cites Gradient-aware for class-imbalanced semi-supervised medical image seg- mentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Gradient-aware for class-imbalanced semi-supervised medical image seg- mentation

Reference 32

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Observation 3d9af97a-275b-4255-bb28-70e1209ce059 · outbound

This paper cites Semi-supervised ct lesion segmen- tation using uncertainty-based data pairing and swapmix.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Semi-supervised ct lesion segmen- tation using uncertainty-based data pairing and swapmix

Reference 33

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This paper cites Deep co-training for semi-supervised image recognition.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Deep co-training for semi-supervised image recognition

Reference 34

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Observation ba87d2ce-2768-415b-95da-1cf35166f96b · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Learning transferable visual models from natural lan- guage supervision

Reference 35

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Observation 01783020-02db-4097-8b5a-8432fa8d572b · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Learning transferable visual models from natural lan- guage supervision

Reference 36

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Observation 41099871-6334-4c95-a020-7168a1b43a2e · outbound

This paper cites Meta-weight-net: Learn- ing an explicit mapping for sample weighting.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Meta-weight-net: Learn- ing an explicit mapping for sample weighting

Reference 37

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Observation 18a61144-8b0d-4ca0-bbe3-808852071d0d · outbound

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

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 38

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Observation 4be4e6d4-05f6-4d49-94b3-55bee24fe2f2 · outbound

This paper cites Semi-supervised segmentation of radiation-induced pul- monary fibrosis from lung ct scans with multi-scale guided dense attention.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Semi-supervised segmentation of radiation-induced pul- monary fibrosis from lung ct scans with multi-scale guided dense attention

Reference 39

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Observation f78fccb5-841e-41d2-96b6-453df9be9a5c · outbound

This paper cites DHC: Dual- debiased heterogeneous co-training framework for class- imbalanced semi-supervised medical image segmenta- tion.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation DHC: Dual- debiased heterogeneous co-training framework for class- imbalanced semi-supervised medical image segmenta- tion

Reference 40

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Observation a7f1e5e9-6fbc-4eb8-91fd-8c73cd9346df · outbound

This paper cites Towards generic semi- supervised framework for volumetric medical image seg- mentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Towards generic semi- supervised framework for volumetric medical image seg- mentation

Reference 41

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Observation 4783a773-7211-4b35-bf84-fbae7ed9aa5c · outbound

This paper cites Debiased learning from naturally imbalanced pseudo- labels.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Debiased learning from naturally imbalanced pseudo- labels

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:59:02.672066Z

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Observation 666057d2-9e08-428f-a51d-ea7957afe19d · outbound

This paper cites Hunting sparsity: Density-guided contrastive learning for semi-supervised semantic segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Hunting sparsity: Density-guided contrastive learning for semi-supervised semantic segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:59:02.650697Z

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Observation d2d2f49b-f037-4dcd-af29-2f2535a23e48 · outbound

This paper cites Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:59:02.631443Z

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Observation 5617004e-a728-4c8b-bb93-552bc8459fb3 · outbound

This paper cites FUSSNet: Fus- ing two sources of uncertainty for semi-supervised medi- cal image segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation FUSSNet: Fus- ing two sources of uncertainty for semi-supervised medi- cal image segmentation

Reference 45

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Observation fd8370c4-73b1-4d9b-9dd9-7debd45a7d18 · outbound

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

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Uncertainty-aware self- ensembling model for semi-supervised 3d left atrium segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:59:02.592027Z

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Observation 2d6960d2-5916-4707-9058-863f89efc97e · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 47

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This paper cites Continual learning for abdominal multi-organ and tumor segmentation.

Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation Continual learning for abdominal multi-organ and tumor segmentation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:59:02.574303Z

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