Paper Citation Record · LEDGER

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2501.04588.

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

pith.paper-citation-record.v1
2501.04588 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

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measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:46:50.394786Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T12:46:52.206029Z

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation 26c01d45-f38b-4fe7-8086-2d445881b5e3 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Memory aware synapses: Learning what (not) to forget

Reference 1

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Observation 0605fd8a-fb30-4e0c-872a-9e9b034f20b0 · outbound

This paper cites Structured crowdsourcing enables convolutional segmenta- tion of histology images.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Structured crowdsourcing enables convolutional segmenta- tion of histology images

Reference 2

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Observation 8e44d3d4-da3c-4637-8a7c-32e56fbd36d2 · outbound

This paper cites Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning

Reference 3

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Observation a02083c9-954f-431a-a0eb-9247cb4beaad · outbound

This paper cites Continual learning strategies for cancer- independent detection of lymph node metastases.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Continual learning strategies for cancer- independent detection of lymph node metastases

Reference 4

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This paper cites From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge

Reference 5

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Observation 3d52acd7-f288-479a-9f9e-771833b7f4bc · outbound

This paper cites Relay learning: a physically secure framework for clinical multi-site deep learning.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Relay learning: a physically secure framework for clinical multi-site deep learning

Reference 6

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Observation a8b43c33-47b7-4574-afaf-2dac20f39d8b · outbound

This paper cites Albumentations: fast and flexible image augmenta- tions.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Albumentations: fast and flexible image augmenta- tions

Reference 7

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Observation 655bba9c-69c2-44f2-89c9-c79536ff0e79 · outbound

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

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity A simple framework for contrastive learning of visual representations

Reference 8

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This paper cites Distance-based detection of out-of-distribution silent failures for covid- 19 lung lesion segmentation.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Distance-based detection of out-of-distribution silent failures for covid- 19 lung lesion segmentation

Reference 9

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Observation 92d7acec-5b51-4a75-bd01-31a7a93629d8 · outbound

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

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Bootstrap your own latent-a new approach to self-supervised learning

Reference 10

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Observation 09b099b2-96e8-4423-93f8-abbf48c3efda · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Distilling the Knowledge in a Neural Network

Reference 11

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Observation e6bc418d-12dd-4df2-8267-6810bd6af534 · outbound

This paper cites Comprehensive ai model de- velopment for gleason grading: From scanning, cloud-based annotation to pathologist-ai interaction.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Comprehensive ai model de- velopment for gleason grading: From scanning, cloud-based annotation to pathologist-ai interaction

Reference 12

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This paper cites Adam: A Method for Stochastic Optimization.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Adam: A Method for Stochastic Optimization

Reference 13

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This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Overcoming catastrophic forgetting in neu- ral networks

Reference 14

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Observation bee739e7-ce2f-4997-a97a-4b222dc68d98 · outbound

This paper cites Clinical applications of con- tinual learning machine learning.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Clinical applications of con- tinual learning machine learning

Reference 15

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This paper cites Di- mensions of health data integrity.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Di- mensions of health data integrity

Reference 16

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Observation 0be32e6f-7ed8-489b-a35a-ee778e04d5d1 · outbound

This paper cites Federated learning for multi- center imaging diagnostics: a simulation study in cardiovas- cular disease.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Federated learning for multi- center imaging diagnostics: a simulation study in cardiovas- cular disease

Reference 17

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This paper cites Federated learning for computational pathology on gigapixel whole slide images.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Federated learning for computational pathology on gigapixel whole slide images

Reference 18

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This paper cites Torchvision the machine-vision package of torch.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Torchvision the machine-vision package of torch

Reference 19

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Observation 38fdad7f-f630-4ebc-8068-cd4336c4bbf4 · outbound

This paper cites Communication- efficient learning of deep networks from decentralized data.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Communication- efficient learning of deep networks from decentralized data

Reference 20

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This paper cites Cancer Research in the Time of COVID-19: A Colombian Narrative.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Cancer Research in the Time of COVID-19: A Colombian Narrative

Reference 21

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Observation e208d776-39d7-48f2-8d35-a9d267c23d45 · outbound

This paper cites Semicol challenge, 2023.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Semicol challenge, 2023

Reference 22

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

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Pytorch: An im- perative style, high-performance deep learning library

Reference 23

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This paper cites Dynamic memory to alleviate catastrophic for- getting in continual learning with medical imaging.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Dynamic memory to alleviate catastrophic for- getting in continual learning with medical imaging

Reference 24

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Observation 337df7e5-978e-45ef-8dff-6f1d1198d496 · outbound

This paper cites Continual atlas-based segmentation of prostate mri.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Continual atlas-based segmentation of prostate mri

Reference 25

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This paper cites icarl: Incremental classifier and representation learning.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity icarl: Incremental classifier and representation learning

Reference 26

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This paper cites Adaptive federated optimization.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Adaptive federated optimization

Reference 27

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This paper cites The future of digital health with federated learning.NPJ dig- ital medicine, 2020.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity The future of digital health with federated learning.NPJ dig- ital medicine, 2020

Reference 28

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Observation a6f716b5-b914-45fd-8f16-cc0310f84bb0 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity U- net: Convolutional networks for biomedical image segmen- tation

Reference 29

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This paper cites Asynchronous federated continual learning.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Asynchronous federated continual learning

Reference 30

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Observation 8ee15b32-dd3b-44ee-812a-77bfb0439d3d · outbound

This paper cites Measuring domain shift for deep learning in histopathology.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Measuring domain shift for deep learning in histopathology

Reference 31

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Observation 357d1c70-3cbc-4c7d-8ee0-ca1de8ec4156 · outbound

This paper cites FrOoDo: Framework for Out-of-Distribution Detection.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity FrOoDo: Framework for Out-of-Distribution Detection

Reference 32

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Observation 328efb22-919e-4cb9-878b-84ea61656b47 · outbound

This paper cites A review of artifacts in histopathol- ogy.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity A review of artifacts in histopathol- ogy

Reference 33

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Observation c564266b-fd1d-43b4-9f37-9da80351555d · outbound

This paper cites Federated stain normalization for compu- tational pathology.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Federated stain normalization for compu- tational pathology

Reference 34

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This paper cites Adap- tive federated learning in resource constrained edge comput- ing systems.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Adap- tive federated learning in resource constrained edge comput- ing systems

Reference 35

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Observation 86572c66-5c5e-48d4-9b01-1be77ab33ce8 · outbound

This paper cites A comparative study of perfor- mance between federated learning and centralized learning using pathological image of endometrial cancer.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity A comparative study of perfor- mance between federated learning and centralized learning using pathological image of endometrial cancer

Reference 36

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Observation cdaa8277-deee-4280-8906-41fd7b8e62b3 · outbound

This paper cites Federated continual learning with weighted inter-client transfer.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Federated continual learning with weighted inter-client transfer

Reference 37

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Observation 6221c602-2ae5-403e-8d75-27fc9b21e89b · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Barlow twins: Self-supervised learning via redundancy reduction

Reference 38

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This paper cites Target: Federated class-continual learning via exemplar-free distillation.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Target: Federated class-continual learning via exemplar-free distillation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:34:42.959502Z

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Observation c96a143b-3469-481d-b3ff-7e0c69a82263 · outbound

This paper cites Federated Learning with Non-IID Data.

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity Federated Learning with Non-IID Data

Reference 40

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Pith citing papers

Observation f5f2ddfb-979d-424a-9436-7f88a88fbce3 · inbound

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey cites this paper.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity

Reference 120

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