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Paper Citation Record · LEDGER

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2411.08936.

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

pith.paper-citation-record.v1
2411.08936 v1

Coverage vector

measured 39 of 39 reference resolution

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measured 39 of 39 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

39 of 39 outbound references displayed

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Outbound references

Observation 33f7a4c2-7ff7-483c-8247-1f87e9789d0b · outbound

This paper cites Review of the current state of whole slide imaging in pathology.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Review of the current state of whole slide imaging in pathology

Reference 1

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Observation d7bc26bd-581a-4988-b332-a13e3107cb98 · outbound

This paper cites Utilizing whole slide images for pathology peer review and working groups.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Utilizing whole slide images for pathology peer review and working groups

Reference 2

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Observation 540a2b85-b589-4532-8ff4-03fd3d4f6260 · outbound

This paper cites Digital imaging in pathology–current applications and challenges.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Digital imaging in pathology–current applications and challenges

Reference 3

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Observation c2217f4a-5ab5-4c44-ac50-a81d13a0835a · outbound

This paper cites Whole slide imaging (wsi) in pathology: current perspectives and future directions.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Whole slide imaging (wsi) in pathology: current perspectives and future directions

Reference 4

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Observation 5ccd5f0d-0304-4d4f-83ea-e63dacdac19d · outbound

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

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images A simple framework for contrastive learning of visual representations

Reference 5

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Observation 263e5f29-ee42-41ea-aea7-996b1dad7b9a · outbound

This paper cites Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning

Reference 6

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Observation f9f0e3a4-74a9-4d66-ad31-9a0cdc56f538 · outbound

This paper cites Efficient quality control of whole slide pathology images with human-in-the-loop training.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Efficient quality control of whole slide pathology images with human-in-the-loop training

Reference 7

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Observation 62b00151-6cc6-4b5b-938f-3fcf8997754a · outbound

This paper cites Deep residual learning for image recognition.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Deep residual learning for image recognition

Reference 8

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Observation 69547ed8-725d-407e-8d3b-fa9cbd235ae0 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 9

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Observation d1baf92a-2e7c-4c1f-9ed7-27a82def6d93 · outbound

This paper cites Regnet: Multimodal sensor registration using deep neural networks.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Regnet: Multimodal sensor registration using deep neural networks

Reference 10

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Observation 773f553e-7499-4a2a-9155-307dbe610d35 · outbound

This paper cites A convnet for the 2020s.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images A convnet for the 2020s

Reference 11

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Observation 49c80c8b-d0c3-4376-83ce-49e54afccd0f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Swin transformer: Hierarchical vision transformer using shifted windows

Reference 12

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Observation 6b19e820-574c-48c1-9d6a-9ac79cbd3357 · outbound

This paper cites Cluster-to-conquer: A framework for end-to-end multi-instance learning for whole slide image classification.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Cluster-to-conquer: A framework for end-to-end multi-instance learning for whole slide image classification

Reference 13

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Observation d9a16bd1-f164-4ff7-9511-5d6db562fbb8 · outbound

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Neural networks: a comprehensive foundation

Reference 14

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Observation b40ec114-1011-4edc-af59-70e9005a7839 · outbound

This paper cites A framework for multiple-instance learning.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images A framework for multiple-instance learning

Reference 15

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Observation a1b9008f-e819-4c61-8d35-c10cf5e033b5 · outbound

This paper cites A survey on vision transformer.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images A survey on vision transformer

Reference 16

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Observation cccd1c63-7399-4300-8963-5eab62fb8407 · outbound

This paper cites Visual Transformers: Token-based Image Representation and Processing for Computer Vision.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Reference 17

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Observation dc9b5dd4-8a9e-40ff-b442-b70a693bf941 · outbound

This paper cites Attention-based deep multiple instance learning.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Attention-based deep multiple instance learning

Reference 18

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Observation 687618a9-6397-49f0-acb4-13009793f9e5 · outbound

This paper cites Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Reference 19

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Observation dd6b915d-2de7-473f-b69b-adf20cf4a45f · outbound

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

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge

Reference 20

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Observation c98f2116-d9f7-4c9d-9b60-a4d30d5b82e6 · outbound

This paper cites Egfr mutation prediction of lung biopsy images using deep learning.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Egfr mutation prediction of lung biopsy images using deep learning

Reference 21

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Observation 55549c45-d0b3-471d-bb8a-084eed98f050 · outbound

This paper cites Deep learning for whole slide image analysis: an overview.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Deep learning for whole slide image analysis: an overview

Reference 22

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Observation bffe57e4-b4a2-4e90-a201-417c85253e3c · outbound

This paper cites Artificial intelligence in digital breast pathology: techniques and applications.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Artificial intelligence in digital breast pathology: techniques and applications

Reference 23

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Observation 288d0ab3-c615-4715-bdd1-a0d8b0385810 · outbound

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Understanding of a convolutional neural network

Reference 24

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Observation 1ed4c9f1-c9bf-4de5-90e0-8e24ae9d3e6b · outbound

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Transformers in vision: A survey

Reference 25

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Observation 27627a32-7aca-41d0-8472-65dbec6759aa · outbound

This paper cites Patch-based convolutional neural network for whole slide tissue image classification.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Patch-based convolutional neural network for whole slide tissue image classification

Reference 26

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Observation 6fac7e5e-da43-4ddd-a051-7a4480f48b95 · outbound

This paper cites Monte-carlo sampling applied to multiple instance learning for histological image classification.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Monte-carlo sampling applied to multiple instance learning for histological image classification

Reference 27

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Observation 9dfe4dd7-2869-4889-9983-d7d5a56753a8 · outbound

This paper cites DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention

Reference 28

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This paper cites Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier

Reference 29

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Observation 76afff0d-6958-467f-b831-3f5abd9dce06 · outbound

This paper cites Dynamic clustering using particle swarm optimization with application in unsupervised image classification.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Dynamic clustering using particle swarm optimization with application in unsupervised image classification

Reference 30

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Observation 6ac46f51-a194-469a-bd23-c778f242743d · outbound

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Multiple instance learning for heterogeneous images: Training a cnn for histopathology

Reference 31

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Observation 62d054c1-b41b-4475-9c52-8bec3f3a4aac · outbound

This paper cites Deep fisher vector coding for whole slide image classification.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Deep fisher vector coding for whole slide image classification

Reference 32

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Convolutional networks with dense connectivity

Reference 33

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This paper cites https://github.com/DigitalSlideArchive/HistomicsTK.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images https://github.com/DigitalSlideArchive/HistomicsTK

Reference 34

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Least squares quantization in pcm

Reference 35

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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Some methods for classification and analysis of multivariate observations

Reference 36

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Observation 279ff1e5-6aab-4290-aec0-89f5b4b2bd27 · outbound

This paper cites Integration k-means clustering method and elbow method for identification of the best customer profile cluster.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Integration k-means clustering method and elbow method for identification of the best customer profile cluster

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-12T21:37:14.680509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:37:14.612582Z digest=sha256:088dd0be1e8ed532484472c5c70dbf5f2d986493656ede9f2cdd545e447604d9

Observation ac5ad2df-36b7-43fd-9fe3-60d27e935e12 · outbound

This paper cites Imagenet large scale visual recognition challenge.International journal of computer vision, 115:211–252, 2015.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images Imagenet large scale visual recognition challenge.International journal of computer vision, 115:211–252, 2015

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-12T21:37:14.615715Z

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source=pdf_text observed=2026-08-12T21:37:14.615715Z digest=sha256:095c4135135429bd76839ea037a630e53a0a6a492e7d535facfb0a2f1b5637df

Observation 144fa084-93a0-492c-80bc-983a96172629 · outbound

This paper cites 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T21:37:14.618877Z

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source=pdf_text observed=2026-08-12T21:37:14.618877Z digest=sha256:b8773a1fe28f529eb42259e56c587f9eb1fb2309d95eb41b7479af8cbac02a2b

Pith citing papers

No inbound Pith citation observations are available.