Paper Citation Record · LEDGER

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector

As of 12 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2412.19208.

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

pith.paper-citation-record.v1
2412.19208 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:53:33.728481Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

23 of 23 outbound references displayed

verified exact0
verified fuzzy15
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a601c65b-bfe9-434b-928c-afbbd097fefa · outbound

This paper cites Structural Compression of Convolutional Neural Networks.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Structural Compression of Convolutional Neural Networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.235521Z

Source-reported events for the cited work

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Observation 0c432aa7-d136-4841-b372-9e657555488f · outbound

This paper cites Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:35.533374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f068b915-92f9-4695-ac8a-f27cd98c3193 · outbound

This paper cites Adaptive feature selection using an autoencoder and classifier: Applied to a radiomics case.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Adaptive feature selection using an autoencoder and classifier: Applied to a radiomics case

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:35.367188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f31455fd-1348-4131-80e6-cd23eef452ad · outbound

This paper cites Radiomics: the process and the challenges.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Radiomics: the process and the challenges

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:35.270275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7079c943-9e72-4420-8fce-6eaead54b3b3 · outbound

This paper cites Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:35.139681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e2e43b28-a2e0-49cb-8cd2-283c10f28de2 · outbound

This paper cites Radiomics: extracting more information from medical images using advanced feature analysis.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Radiomics: extracting more information from medical images using advanced feature analysis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:35.023654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 666ddb57-f388-4f1c-a406-a24176a1a95e · outbound

This paper cites An explainable machine learning framework for multiple medical datasets classification.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector An explainable machine learning framework for multiple medical datasets classification

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.954991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1eb14e21-05d6-40f8-b959-2532c29da451 · outbound

This paper cites Interpretable and explainable machine learning: a methods-centric overview with concrete examples.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Interpretable and explainable machine learning: a methods-centric overview with concrete examples

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.778058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4147bbe7-6a5f-4a09-8c4f-2ebe9c1c4483 · outbound

This paper cites Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast mri.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast mri

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.748489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8cbb4a1d-3d9a-42b9-b4f0-26d876eee2a8 · outbound

This paper cites Explanation of machine learning models using shapley additive explanation and application for real data in hospital.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Explanation of machine learning models using shapley additive explanation and application for real data in hospital

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.723198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d53b5717-5c42-4d2f-94a3-bb036bec3942 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Attention U-Net: Learning Where to Look for the Pancreas

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.483878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 696f5a40-504a-4c17-8340-e4a167592b0e · outbound

This paper cites Local interpretable model-agnostic explanations for classification of lymph node metastases.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Local interpretable model-agnostic explanations for classification of lymph node metastases

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.545537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b8966a87-c9fd-4934-a536-30b3b5262359 · outbound

This paper cites Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.521528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f75065a6-3514-43be-bb08-ea16268a252d · outbound

This paper cites Book review: Max kuhn and kjell johnson.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Book review: Max kuhn and kjell johnson

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.398797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 47a635bc-2c68-4cee-a792-6d6875d7fc24 · outbound

This paper cites why should i trust you?.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector why should i trust you?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.509659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab05d509-8e56-4b51-893a-5874d178a021 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.530707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd82240-8181-47f7-8c6a-31e14ad0c9a4 · outbound

This paper cites Striving for Simplicity: The All Convolutional Net.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Striving for Simplicity: The All Convolutional Net

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.598991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c87335e6-7fa8-4968-927e-969b63a2c775 · outbound

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

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.638363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 265ffcc6-fb0a-4403-b279-c0cd9d8c045f · outbound

This paper cites Detecting Statistical Interactions from Neural Network Weights.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Detecting Statistical Interactions from Neural Network Weights

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.700765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98e1631b-6fa1-4448-9a77-a2198d103362 · outbound

This paper cites Sunet: A lesion regularized model for simultaneous diabetic retinopathy and diabetic macular edema grading.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Sunet: A lesion regularized model for simultaneous diabetic retinopathy and diabetic macular edema grading

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.292352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 234c89d5-0f3e-40fe-aa62-d10b02f2b384 · outbound

This paper cites Visualizing and understanding convolutional networks.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Visualizing and understanding convolutional networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T00:53:33.715385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 263c1f0a-970f-4869-8f09-c42f774d5c1a · outbound

This paper cites Ibex: an open infrastructure software platform to facilitate collaborative work in radiomics.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Ibex: an open infrastructure software platform to facilitate collaborative work in radiomics

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.159556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f549cf63-25ac-424e-99ef-48ef78cfdeb7 · outbound

This paper cites Learning deep features for discriminative localization.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Learning deep features for discriminative localization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:53:34.019895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.