Paper Citation Record · LEDGER

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2501.08094.

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

pith.paper-citation-record.v1
2501.08094 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:34:46.402282Z

measured 37 of 37 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 2026-08-06T06:05:44.558926Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T06:05:44.714266Z

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 50425a90-1031-402d-8585-6f0706018518 · outbound

This paper cites an unresolved cited work.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Unresolved cited work

Reference 1

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Observation 483b8965-eeae-43ee-bfcd-1f44446dac93 · outbound

This paper cites The 2021 who classification of lung tumors: impact of ad- vances since 2015.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns The 2021 who classification of lung tumors: impact of ad- vances since 2015

Reference 2

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Observation 59fc100d-c7d0-4d5f-bbc8-d4cf51874564 · outbound

This paper cites A grading system for in- vasive pulmonary adenocarcinoma: a proposal from the international association for the study of lung cancer pathology committee.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns A grading system for in- vasive pulmonary adenocarcinoma: a proposal from the international association for the study of lung cancer pathology committee

Reference 3

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Observation 8346f812-7303-4e48-a146-44f4c0322195 · outbound

This paper cites Overcoming the in- terobserver variability in lung adenocarcinoma sub- typing: A clustering approach to establish a ground truth for downstream applications.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Overcoming the in- terobserver variability in lung adenocarcinoma sub- typing: A clustering approach to establish a ground truth for downstream applications

Reference 4

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Observation 5323e4bc-1376-403e-8c18-84beb10b8fe1 · outbound

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

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Reference 5

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Observation 1f7d9f4a-e080-4b36-ac21-8286fb5e664b · outbound

This paper cites Social net- work analysis of cell networks improves deep learn- ing for prediction of molecular pathways and key mutations in colorectal cancer.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Social net- work analysis of cell networks improves deep learn- ing for prediction of molecular pathways and key mutations in colorectal cancer

Reference 6

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Observation 5b54c105-64b8-4eb7-bf03-76b566e127c9 · outbound

This paper cites Growth pattern fingerprinting for auto- matic analysis of lung adenocarcinoma overall sur- vival 2023.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Growth pattern fingerprinting for auto- matic analysis of lung adenocarcinoma overall sur- vival 2023

Reference 7

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Observation c7c30ae4-1957-475f-ab40-28a3ad60bc23 · outbound

This paper cites Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Reference 8

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Observation 7a40a946-456d-4f7b-9572-9fd2f8880a65 · outbound

This paper cites Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

Reference 9

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Observation 1ec68e9a-9924-4d35-8979-1b1caac37299 · outbound

This paper cites E2efp-mil: End-to-end and high-generalizability weakly super- vised deep convolutional network for lung cancer classification from whole slide image.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns E2efp-mil: End-to-end and high-generalizability weakly super- vised deep convolutional network for lung cancer classification from whole slide image

Reference 10

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Observation b2962892-8269-4d0c-a0dd-bb6053fbbc80 · outbound

This paper cites Pixel-level classification of five histologic pat- terns of lung adenocarcinoma.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Pixel-level classification of five histologic pat- terns of lung adenocarcinoma

Reference 11

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Observation c8b8afe1-de4f-4da5-b9b2-a0668fec5936 · outbound

This paper cites H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma

Reference 12

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Observation 761a5564-2147-495f-996e-b43239637293 · outbound

This paper cites Cross-stream interactions: Segmentation of lung adenocarcinoma growth patterns.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Cross-stream interactions: Segmentation of lung adenocarcinoma growth patterns

Reference 13

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Observation 31195744-2184-473c-bb9e-3e8877f000c8 · outbound

This paper cites Detisseg: A dual-encoder network for tis- sue semantic segmentation of histopathology image.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Detisseg: A dual-encoder network for tis- sue semantic segmentation of histopathology image

Reference 14

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Observation 5f80e7d7-8cd5-48bb-849c-7c9481cb1fd7 · outbound

This paper cites Hover-net: Simultaneous segmen- tation and classification of nuclei in multi-tissue histology images.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Hover-net: Simultaneous segmen- tation and classification of nuclei in multi-tissue histology images

Reference 15

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Observation c37cee33-21d4-446e-b346-2c5bb2bc3432 · outbound

This paper cites A semi-supervised learning framework for micropapillary adenocarcinoma detection.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns A semi-supervised learning framework for micropapillary adenocarcinoma detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:46.657457Z

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Observation c69fdea4-b970-4e85-a49a-8cadc0d0381b · outbound

This paper cites Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides

Reference 17

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Observation b46be8e5-d1f3-4800-96c3-adf65a3be0ee · outbound

This paper cites Comparative analysis of machine learning approaches to classify tu- mor mutation burden in lung adenocarcinoma us- ing histopathology images.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Comparative analysis of machine learning approaches to classify tu- mor mutation burden in lung adenocarcinoma us- ing histopathology images

Reference 18

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Observation 4e38f79a-57ab-46d3-9f86-b72053d6dcbc · outbound

This paper cites Pan- nuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Pan- nuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification

Reference 19

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Observation e91ca404-d6a8-41ac-8078-c9c58dff2fea · outbound

This paper cites Pathology and epidemiology of cancer.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Pathology and epidemiology of cancer

Reference 20

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Observation 5386cf9b-dd47-4f7c-bca6-83e4f8e73cc6 · outbound

This paper cites Focal loss for dense object detec- tion.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Focal loss for dense object detec- tion

Reference 21

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Observation b5d3d0fe-7084-41c7-a827-55457e4bb356 · outbound

This paper cites The Cancer Genome Atlas Pan- Cancer analysis project.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns The Cancer Genome Atlas Pan- Cancer analysis project

Reference 22

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Observation 08bf6cbe-2131-4daf-8cb9-ead43ebcda81 · outbound

This paper cites The national lung screening trial: overview and study de- sign.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns The national lung screening trial: overview and study de- sign

Reference 23

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Observation 317a3ff9-4dad-45b2-bfb3-929d07508446 · outbound

This paper cites The artificial intelligence-based model anorak improves histopathological grading of lung adeno- carcinoma.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns The artificial intelligence-based model anorak improves histopathological grading of lung adeno- carcinoma

Reference 24

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CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Unresolved cited work

Reference 25

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Observation 3a8ad5c7-d20e-4bb5-b84b-bd4fee06aa74 · outbound

This paper cites Deepluad: An efficient approach for lung adenocarcinoma pattern classification.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Deepluad: An efficient approach for lung adenocarcinoma pattern classification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:46.553759Z

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Observation cb83a556-ac8c-4b04-9bd9-730e6ad020cf · outbound

This paper cites Deep learning- based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Deep learning- based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma

Reference 27

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Observation e9b59303-d4e7-4ec7-96c3-55b858456369 · outbound

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

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 28

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This paper cites The underlying tumor genomics of predominant histologic subtypes in lung adenocarcinoma.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns The underlying tumor genomics of predominant histologic subtypes in lung adenocarcinoma

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:46.532893Z

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This paper cites Immunogenomic profiling of lung adenocarcinoma reveals high-grade growth patterns 20 are associated with an immunogenic tumor microen- vironment.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Immunogenomic profiling of lung adenocarcinoma reveals high-grade growth patterns 20 are associated with an immunogenic tumor microen- vironment

Reference 30

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Observation bab2a789-fe8c-4dd0-8667-095e0c7bec85 · outbound

This paper cites High tumor mutation burden pre- dicts favorable outcome among patients with aggres- sive histological subtypes of lung adenocarcinoma: A population-based single-institution study.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns High tumor mutation burden pre- dicts favorable outcome among patients with aggres- sive histological subtypes of lung adenocarcinoma: A population-based single-institution study

Reference 31

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This paper cites Fda approval summary: pembrolizumab for the treatment of tu- mor mutational burden–high solid tumors.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Fda approval summary: pembrolizumab for the treatment of tu- mor mutational burden–high solid tumors

Reference 32

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Observation b687166f-7cdf-4e9f-a71c-46ebcd1bd1b7 · outbound

This paper cites Molecu- lar heterogeneity in histomorphologic subtypes of lung adeno carcinoma represents a challenge for treatment decision.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Molecu- lar heterogeneity in histomorphologic subtypes of lung adeno carcinoma represents a challenge for treatment decision

Reference 33

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Observation afddfc75-a37e-4f6b-b546-5f52e3b2d435 · outbound

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CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Unresolved cited work

Reference 34

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This paper cites Slide- graph+: Whole slide image level graphs to predict her2 status in breast cancer.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns Slide- graph+: Whole slide image level graphs to predict her2 status in breast cancer

Reference 35

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This paper cites A mathematical theory of communication.

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns A mathematical theory of communication

Reference 36

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Pith citing papers

Observation 98e6186a-064d-4a35-a30b-5676a29d7f88 · inbound

Bag-of-Visual-Words for Spatial Mapping of Lung Adenocarcinoma Growth Patterns cites this paper.

Bag-of-Visual-Words for Spatial Mapping of Lung Adenocarcinoma Growth Patterns CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns

Reference 7

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