Paper Citation Record · LEDGER

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.06943.

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

pith.paper-citation-record.v1
1908.06943 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:12:00.462969Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 857d148a-fa2d-4ce6-9f68-2016df8f761d · outbound

This paper cites & Hinton, G.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Hinton, G

Reference 1

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This paper cites Deep learning in neural networks: An overview.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Deep learning in neural networks: An overview

Reference 2

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 3

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This paper cites & Hinton, G.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Hinton, G

Reference 4

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 6

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This paper cites & Brox, T.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Brox, T

Reference 7

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 8

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Samek, W

Reference 9

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 10

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Observation 268d41ea-ad7c-4651-91cd-419b7d40008c · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 11

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 12

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This paper cites Understanding Neural Networks Through Deep Visualization.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Understanding Neural Networks Through Deep Visualization

Reference 13

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 14

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 15

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Observation c46f38ea-28e7-4b5d-8bba-2fac60099539 · outbound

This paper cites T., Alber, M., Müller, K.-R.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods T., Alber, M., Müller, K.-R

Reference 16

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Observation c6aedf8c-5bf7-4182-8fc4-df339924568c · outbound

This paper cites & Müller, K.-R.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Müller, K.-R

Reference 17

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Observation 53edd61b-7c22-4cb3-a0d0-aaf6793d97fe · outbound

This paper cites M., Cohen, T.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods M., Cohen, T

Reference 18

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Observation d634662f-dad4-4e9d-801c-9a21c85712c1 · outbound

This paper cites Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Reference 19

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 20

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 21

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This paper cites Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology

Reference 22

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 23

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Tsai, C.-F

Reference 24

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This paper cites Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic proﬁling.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic proﬁling

Reference 25

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 27

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Sun, J

Reference 28

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Observation 95663fcf-051a-43e4-ba34-3a21ea94f806 · outbound

This paper cites Large scale tissue histopathology image classiﬁcation, segmentation, and visualization via deep convolutional activation features.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Large scale tissue histopathology image classiﬁcation, segmentation, and visualization via deep convolutional activation features

Reference 29

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This paper cites Deep Learning for Identifying Metastatic Breast Cancer.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Deep Learning for Identifying Metastatic Breast Cancer

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Hajirasouliha, I

Reference 31

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

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This paper cites A., Ovalle, J.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods A., Ovalle, J

Reference 34

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Müller, H

Reference 36

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This paper cites Predicting cancer outcomes from histology and genomics using convolutional networks.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Predicting cancer outcomes from histology and genomics using convolutional networks

Reference 37

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This paper cites Detecting Cancer Metastases on Gigapixel Pathology Images.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Detecting Cancer Metastases on Gigapixel Pathology Images

Reference 38

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Unresolved cited work

Reference 39

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This paper cites http://cancergenome.nih.gov.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods http://cancergenome.nih.gov

Reference 40

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This paper cites Caffe: Convolutional architecture for fast feature embedding.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Caffe: Convolutional architecture for fast feature embedding

Reference 41

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Samek, W

Reference 42

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This paper cites & Müller, K.-R.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods & Müller, K.-R

Reference 43

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This paper cites biased" (left) and the.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods biased" (left) and the

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Pith citing papers

No inbound Pith citation observations are available.