IRS: Incremental Relationship-guided Segmentation for Digital Pathology | Paper Citation Record

Observation 63d8af83-a90e-49b5-b7fa-894dc2745922 · outbound

This paper cites Three types of incremental learning,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Three types of incremental learning,

Reference 1

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This paper cites Ella: An efficient lifelong learning algorithm,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Ella: An efficient lifelong learning algorithm,

Reference 2

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Observation c182c6af-bd6b-46bc-8b89-99e1b61696c7 · outbound

This paper cites Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization,

Reference 3

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Observation d55785c5-dc4b-4eea-a941-8517e6fc0afa · outbound

This paper cites Model Zoo: A Growing "Brain" That Learns Continually.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Model Zoo: A Growing "Brain" That Learns Continually

Reference 4

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This paper cites CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks

Reference 5

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This paper cites An efficient domain-incremental learning approach to drive in all weather condi- tions,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology An efficient domain-incremental learning approach to drive in all weather condi- tions,

Reference 6

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Observation 938bad9f-b34b-4ccb-ac4a-89a8e95629bf · outbound

This paper cites Continual learning with hypernetworks.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Continual learning with hypernetworks

Reference 7

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Observation 862b0bbf-6320-4a46-a16a-c8583788f65f · outbound

This paper cites Continual learning with deep generative replay,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Continual learning with deep generative replay,

Reference 8

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Observation a967a8e5-6e1a-45a6-a804-ae58f54e7ea3 · outbound

This paper cites Brain-inspired replay for continual learning with artificial neural networks,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Brain-inspired replay for continual learning with artificial neural networks,

Reference 9

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Observation 286f97da-8aa7-4d92-87dc-dcbb184c3d8c · outbound

This paper cites A systematic collection of medical image datasets for deep learning,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology A systematic collection of medical image datasets for deep learning,

Reference 10

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Observation 2e048caf-a34a-47a7-a23b-181e4e9ac4fd · outbound

This paper cites A systematic approach of data collection and analysis in medical imaging research,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology A systematic approach of data collection and analysis in medical imaging research,

Reference 11

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Observation c7f9bdb6-5489-4176-b606-bf54baf1c18c · outbound

This paper cites Continual learning in medical devices: Fda’s action plan and beyond,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Continual learning in medical devices: Fda’s action plan and beyond,

Reference 12

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Observation 44db5b8c-c581-44f4-87db-6e09b8e8c5cc · outbound

This paper cites Passive data collection and use in healthcare: A systematic review of ethical issues,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Passive data collection and use in healthcare: A systematic review of ethical issues,

Reference 13

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Observation 54d879f1-d941-420d-b435-12bd44225d3c · outbound

This paper cites Data collection theory in healthcare research: the minimum dataset in quanti- tative studies,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Data collection theory in healthcare research: the minimum dataset in quanti- tative studies,

Reference 14

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Observation 3989115d-c174-4d76-b154-857297ad9de7 · outbound

This paper cites Towards flexible mobile data collection in healthcare,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Towards flexible mobile data collection in healthcare,

Reference 15

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Observation 83038a1e-34df-45c3-b5d2-6efdbd7642ce · outbound

This paper cites Digital pathology and computational image analysis in nephropathol- ogy,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Digital pathology and computational image analysis in nephropathol- ogy,

Reference 16

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Observation 76f28544-f675-4026-a4ad-ee7fd9460887 · outbound

This paper cites Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging,

Reference 17

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Observation 89c865be-1fe3-45c6-9ce2-92bca6632f1f · outbound

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

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Continual learning for abdominal multi-organ and tumor segmentation,

Reference 18

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Observation a7db80be-479a-4ef7-b25c-ba055b4802a0 · outbound

This paper cites Low- rank mixture-of-experts for continual medical image segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Low- rank mixture-of-experts for continual medical image segmentation,

Reference 19

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Observation 774f00d9-10e8-4333-991d-1a3c17a971eb · outbound

This paper cites Lifelong nnu-net: a framework for standardized medical continual learning,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Lifelong nnu-net: a framework for standardized medical continual learning,

Reference 20

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Observation 6bfdbb00-a3f5-4ec0-8b61-772315113c7b · outbound

This paper cites Deep hierarchical semantic segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Deep hierarchical semantic segmentation,

Reference 21

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Observation 7ee7a9be-be86-4b2d-9e0e-16ad67c76e8a · outbound

This paper cites Unsupervised hierarchical semantic segmentation with multiview cosegmentation and clustering transformers,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Unsupervised hierarchical semantic segmentation with multiview cosegmentation and clustering transformers,

Reference 22

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Observation 45941d5a-146c-4112-8073-e20ae74ff02e · outbound

This paper cites Prpseg: Universal proposition learning for panoramic renal pathology segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Prpseg: Universal proposition learning for panoramic renal pathology segmentation,

Reference 23

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Observation e28aebb8-20c1-4bf6-9f05-05ccbcf9d34a · outbound

This paper cites Artificial intelligence in renal pathology: Current status and future,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Artificial intelligence in renal pathology: Current status and future,

Reference 24

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Observation d09dc920-47b6-436a-93f7-2b25b34a90c1 · outbound

This paper cites Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules,

Reference 25

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Observation fd4ee40c-2431-425d-9852-20e4be6bf690 · outbound

This paper cites CNN Cascades for Segmenting Whole Slide Images of the Kidney.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology CNN Cascades for Segmenting Whole Slide Images of the Kidney

Reference 26

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Observation 79f938d0-a087-4ff1-863f-13e56be6cb9f · outbound

This paper cites Glomerulus classification and detection based on convolutional neural networks,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Glomerulus classification and detection based on convolutional neural networks,

Reference 27

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Observation 8708e80d-2b17-416d-b02b-fb2c9cd834db · outbound

This paper cites Glomerulosclerosis identification in whole slide images using semantic segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Glomerulosclerosis identification in whole slide images using semantic segmentation,

Reference 28

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Observation 9bf7dbdb-7178-47f7-8816-c1db72682742 · outbound

This paper cites An integrated iterative annotation technique for easing neural network training in medical image analysis,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology An integrated iterative annotation technique for easing neural network training in medical image analysis,

Reference 29

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Observation 88eea743-7cad-4c02-ad30-c96917ec6c9d · outbound

This paper cites Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology,

Reference 30

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Observation 92df080e-f516-482f-9528-e1e343323dac · outbound

This paper cites Segmentation of glomeruli within trichrome images using deep learning,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Segmentation of glomeruli within trichrome images using deep learning,

Reference 31

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Observation c380c721-088e-477f-a5ae-334993ed9281 · outbound

This paper cites Automatic nucleus segmentation with mask-rcnn,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Automatic nucleus segmentation with mask-rcnn,

Reference 32

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Observation 37e9b4fe-418c-4be7-943a-faf356bd0bf5 · outbound

This paper cites Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation

Reference 33

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Observation a450754b-6238-489e-9fb2-cb00dbb24541 · outbound

This paper cites Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image,

Reference 34

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Observation 0aa6c4f9-45a8-4256-9366-9500aad78828 · outbound

This paper cites Self reinforcing multi-class transformer for kidney glomerular basement membrane segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Self reinforcing multi-class transformer for kidney glomerular basement membrane segmentation,

Reference 35

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Observation 18ce508b-6aae-4c57-9b84-8f28422e706d · outbound

This paper cites Automatic evaluation of histological prognostic factors using two consecutive convolutional neural networks on kidney samples,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Automatic evaluation of histological prognostic factors using two consecutive convolutional neural networks on kidney samples,

Reference 36

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Observation 363ce123-c9b0-4170-807a-ca941e86d245 · outbound

This paper cites Multi-structure segmentation from partially labeled datasets. application to body compo- sition measurements on ct scans,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Multi-structure segmentation from partially labeled datasets. application to body compo- sition measurements on ct scans,

Reference 37

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Observation c2a22b41-39f9-473b-9064-cbf9ca839a13 · outbound

This paper cites Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction,

Reference 38

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Observation abb0677a-2670-4272-b51f-9b2dcac5a787 · outbound

This paper cites Deep learning–based segmentation and quantification in experimental kidney histopathology,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Deep learning–based segmentation and quantification in experimental kidney histopathology,

Reference 39

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Observation 180a82c9-a2a7-4b41-a416-af3e9fdb15e3 · outbound

This paper cites Med3D: Transfer Learning for 3D Medical Image Analysis.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Med3D: Transfer Learning for 3D Medical Image Analysis

Reference 40

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Observation 0913a1a8-dfec-45d4-8d14-158f488d352a · outbound

This paper cites Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets,

Reference 41

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Observation c5894c67-3699-490d-a5a4-2d8fff4bf5fc · outbound

This paper cites Omni-seg: A scale-aware dynamic 10 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2025 network for renal pathological image segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Omni-seg: A scale-aware dynamic 10 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2025 network for renal pathological image segmentation,

Reference 42

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Observation e6eec956-b750-4f2d-9ff7-83fefe9685b1 · outbound

This paper cites Segmentation of tumour regions for tubule formation assessment on breast cancer histopathology images,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Segmentation of tumour regions for tubule formation assessment on breast cancer histopathology images,

Reference 43

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Observation bb268798-ef7c-4d5a-a2f0-d10f1792b98c · outbound

This paper cites Adversarial learning with data selection for cross-domain histopathological breast cancer segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Adversarial learning with data selection for cross-domain histopathological breast cancer segmentation,

Reference 44

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Observation d01405d0-a507-48de-aabd-a243f6bfbaac · outbound

This paper cites Muscle: Multi-task self-supervised continual learning to pre-train deep models for x-ray images of multiple body parts,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Muscle: Multi-task self-supervised continual learning to pre-train deep models for x-ray images of multiple body parts,

Reference 45

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Observation 2e86a629-2b66-4424-a05b-638e91f39a07 · outbound

This paper cites Universeg: Universal medical image segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Universeg: Universal medical image segmentation,

Reference 46

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Observation 1d85edef-7eb7-448d-b3d6-896a15e188b0 · outbound

This paper cites Continual self-supervised learning: Towards universal multi-modal medical data representation learning,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Continual self-supervised learning: Towards universal multi-modal medical data representation learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:33.128703Z

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Observation 02ddefe3-15b0-4381-bb99-63dead05b341 · outbound

This paper cites Continual segment: Towards a single, unified and non- forgetting continual segmentation model of 143 whole-body organs in ct scans,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Continual segment: Towards a single, unified and non- forgetting continual segmentation model of 143 whole-body organs in ct scans,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:32.980944Z

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Observation 1f55c544-9f14-4dcc-b178-a0ee8e7513b2 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:32.853201Z

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Observation a8540285-5f7d-4a9d-aa91-17cdcaba8555 · outbound

This paper cites EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 50

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Observation c7ddffe5-0cbf-48ad-8987-58c1f4ae77f0 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 51

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Observation 823af435-6111-49ab-9316-6ac4174618dc · outbound

This paper cites Plop: Learning without forgetting for continual semantic segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Plop: Learning without forgetting for continual semantic segmentation,

Reference 52

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Observation 4a7506a3-8fcb-4511-8911-7c2c82f458d1 · outbound

This paper cites Modeling the background for incremental learning in semantic segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Modeling the background for incremental learning in semantic segmentation,

Reference 53

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This paper cites Incrementer: Transformer for class-incremental semantic segmentation with knowl- edge distillation focusing on old class,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Incrementer: Transformer for class-incremental semantic segmentation with knowl- edge distillation focusing on old class,

Reference 54

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Observation c84cb10a-2c85-4366-9e65-624779c3c14f · outbound

This paper cites Comformer: Continual learn- ing in semantic and panoptic segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Comformer: Continual learn- ing in semantic and panoptic segmentation,

Reference 55

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This paper cites Incremental learning techniques for semantic segmentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Incremental learning techniques for semantic segmentation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:32.599071Z

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Observation 3b736072-e5c7-4ffd-afd0-0578af3d865b · outbound

This paper cites Class similarity weighted knowledge distillation for continual semantic seg- mentation,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Class similarity weighted knowledge distillation for continual semantic seg- mentation,

Reference 57

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This paper cites Towards a general-purpose foundation model for computational pathology,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology Towards a general-purpose foundation model for computational pathology,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:32.440456Z

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This paper cites A whole-slide foundation model for digital pathology from real-world data,.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology A whole-slide foundation model for digital pathology from real-world data,

Reference 59

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