Paper Citation Record · LEDGER

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.12012.

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

pith.paper-citation-record.v1
2507.12012 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:02:40.394609Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7cb9aeb5-4daf-4e83-a7de-f6e491d3646d · outbound

This paper cites Current Status in Testing for Nonalcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH),.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Current Status in Testing for Nonalcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH),

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:46.723060Z

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Observation a64f37e7-1fd5-4ce3-aa4a-e6b27187a48c · outbound

This paper cites Diagnostic and treatment implications of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Diagnostic and treatment implications of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:46.555657Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d9f4cd71-12e1-490f-9506-04e9c0d70306 · outbound

This paper cites Sampling variability of liver biopsy in nonalcoholic fatty liver disease,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Sampling variability of liver biopsy in nonalcoholic fatty liver disease,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:46.432153Z

Source-reported events for the cited work

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Observation aa5683c7-bb8f-43b7-b032-c8965db871e2 · outbound

This paper cites Effects of Liver Biopsy Sample Length and Number of Readings on Sampling Variability in Nonalcoholic Fatty Liver Disease,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Effects of Liver Biopsy Sample Length and Number of Readings on Sampling Variability in Nonalcoholic Fatty Liver Disease,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:46.290521Z

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Observation d67dcd5d-43be-4bbb-995d-3631abafc237 · outbound

This paper cites Noninvasive biomarkers in NAFLD and NASH — current progress and future promise,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Noninvasive biomarkers in NAFLD and NASH — current progress and future promise,

Reference 5

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Observation 5176d336-799e-41d1-bd72-854b1e122bf8 · outbound

This paper cites Deep learning enables pathologist-like scoring of NASH models,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Deep learning enables pathologist-like scoring of NASH models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:45.968329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a31deebe-d02a-4166-843d-b871e85cf262 · outbound

This paper cites Training of deep convolutional neural networks to identify critical liver alterations in histopathology image samples,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Training of deep convolutional neural networks to identify critical liver alterations in histopathology image samples,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:45.720789Z

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Observation ef1879d1-3ac9-4782-a805-101b10a6d8b7 · outbound

This paper cites Application of Machine Learn- ing Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Application of Machine Learn- ing Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:45.535543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 390a2711-1a09-46ce-a8a3-c9183438b404 · outbound

This paper cites Development and validation of a deep learning system for staging liver fibrosis by using contrast agent–enhanced CT images in the liver,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Development and validation of a deep learning system for staging liver fibrosis by using contrast agent–enhanced CT images in the liver,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:45.371675Z

Source-reported events for the cited work

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Observation 2815e9d8-0d0e-447a-81f4-af84df71ea3a · outbound

This paper cites Correlation of histologic, imaging, and artificial intelligence features in nafld patients, derived from gd-eob-dtpa-enhanced mri: a proof-of- concept study,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Correlation of histologic, imaging, and artificial intelligence features in nafld patients, derived from gd-eob-dtpa-enhanced mri: a proof-of- concept study,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:45.120076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 243401ac-8a53-4589-9b53-7753bc2f384d · outbound

This paper cites Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:44.962511Z

Source-reported events for the cited work

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Observation bc6acbbd-bbbd-4856-91b0-0184fa7c5487 · outbound

This paper cites Unsupervised Deep Embedding for Clustering Analysis,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Unsupervised Deep Embedding for Clustering Analysis,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:44.812049Z

Source-reported events for the cited work

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Observation 7a152df5-53d6-44ec-b832-0a36cd1b01d9 · outbound

This paper cites Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:02:41.588369Z

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Observation 00e6baeb-0bf7-4bba-a479-568ed6390503 · outbound

This paper cites Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:44.517503Z

Source-reported events for the cited work

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Observation c397908c-6506-4e1e-94e7-71218d1d5fdb · outbound

This paper cites Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Reference 15

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Source-reported events for the cited work

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Observation 3ee05877-e38f-4b3a-8252-bc2a5778b902 · outbound

This paper cites Joint Unsupervised Learning of Deep Representations and Image Clusters,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Joint Unsupervised Learning of Deep Representations and Image Clusters,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:44.372033Z

Source-reported events for the cited work

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Observation 522fdddd-d41e-4613-b59c-ac435cbc4ee6 · outbound

This paper cites Deep Adaptive Image Clustering,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Deep Adaptive Image Clustering,

Reference 17

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Observation 308e085d-49e1-44ce-8288-0d7fd860f5c9 · outbound

This paper cites Learning Discrete Representations via Information Maximizing Self-Augmented Training.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Learning Discrete Representations via Information Maximizing Self-Augmented Training

Reference 18

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Source-reported events for the cited work

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Observation 1494aa7e-55f7-4aa3-a0d0-d81bc4afbe55 · outbound

This paper cites Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:02:40.922896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af5d18b1-40f5-454e-acec-a2ee2d80617e · outbound

This paper cites Semi-supervised learning with deep embed- ded clustering for image classification and segmentation,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Semi-supervised learning with deep embed- ded clustering for image classification and segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:43.972860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9410d294-a4db-4d00-9841-ea3eec213f05 · outbound

This paper cites Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:43.766136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e0561f30-5e8e-4056-9488-9f2cb1f68cf1 · outbound

This paper cites Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:43.584325Z

Source-reported events for the cited work

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Observation 6d02318f-42ae-4ac8-8709-c47bd7711e0a · outbound

This paper cites Data heterogeneity and continual machine learning for medical image anal- ysis,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Data heterogeneity and continual machine learning for medical image anal- ysis,

Reference 23

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:02:40.754199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fea9e377-f5a3-4d83-8e63-688d066f6134 · outbound

This paper cites Least Squares Quantization in PCM,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Least Squares Quantization in PCM,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:43.392129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 952fa1f8-37fc-4e4a-9cc9-557ddf92ad71 · outbound

This paper cites Video Google: A Text Retrieval Approach to Object Matching in Videos,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Video Google: A Text Retrieval Approach to Object Matching in Videos,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:43.139493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 657bcba1-6d87-4eb9-ac0f-902979d01ae3 · outbound

This paper cites Random Forests,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Random Forests,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:42.821361Z

Source-reported events for the cited work

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Observation 4a4a4b71-fd28-4aa9-8006-c9342bffe6ac · outbound

This paper cites Modern hierarchical, agglomerative clustering algorithms,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Modern hierarchical, agglomerative clustering algorithms,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:42.600337Z

Source-reported events for the cited work

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Observation 9f49eca2-b13c-4375-99c1-a9ec8fc364e5 · outbound

This paper cites Lucas, P.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Lucas, P

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:42.392415Z

Source-reported events for the cited work

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Observation 4f77b5cd-ef00-4c86-8598-7bdd88dddeee · outbound

This paper cites The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:42.190596Z

Source-reported events for the cited work

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Observation dffb58d0-3922-4cda-8d9a-76f6adc84078 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease PyTorch: An Imperative Style, High-Performance Deep Learning Library,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:41.995816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fbaeac77-3afd-4cf9-a706-7c35345ce120 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease U-net: Convolutional networks for biomedical image segmentation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:41.794137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation acac57f9-63b0-4d9e-8205-720b41b10fb9 · outbound

This paper cites Modern hierarchical, agglomerative clustering algorithms.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Modern hierarchical, agglomerative clustering algorithms

Reference 2011

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Source-reported events for the cited work

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Observation 5869e421-4663-4056-bac7-ee456df87f4b · outbound

This paper cites Unsupervised Deep Embedding for Clustering Analysis.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Unsupervised Deep Embedding for Clustering Analysis

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T17:02:38.301430Z

Source-reported events for the cited work

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Observation db6462b3-19d5-4d7c-9818-3b4c1ce73b11 · outbound

This paper cites Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization.

Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

Reference 2017

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:02:41.388023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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