Paper Citation Record · LEDGER

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2505.19110.

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

pith.paper-citation-record.v1
2505.19110 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:23:35.498113Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

26 of 26 outbound references displayed

verified exact1
verified fuzzy18
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73475c68-bc4b-4f2b-8f3c-523d7844ece0 · outbound

This paper cites Cerebral white matter myelination and relations to age, gender, and cognition: a selective review.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Cerebral white matter myelination and relations to age, gender, and cognition: a selective review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:39.490754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2b39746d-551f-40b0-870c-aa883d06824c · outbound

This paper cites Isolating sources of disentanglement in variational autoencoders.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Isolating sources of disentanglement in variational autoencoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:33.442953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02dc271f-62ae-44f4-921a-c39b6d239fa5 · outbound

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

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging A simple framework for contrastive learning of visual representations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:33.482094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 004d1dd9-5f47-4f0d-a17a-34e2f8c4823d · outbound

This paper cites Tractgraphcnn: anatomically informed graph cnn for classification using diffusion mri tractography.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Tractgraphcnn: anatomically informed graph cnn for classification using diffusion mri tractography

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:39.223058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 558fc613-b8c6-46d9-b6b9-6b8cf934393e · outbound

This paper cites A separability-based approach to quantifying generalization: which layer is best?.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging A separability-based approach to quantifying generalization: which layer is best?

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:23:35.730918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5cb4ffc-2009-4511-8d7b-146874454bbd · outbound

This paper cites Variational autoencoders for generating synthetic tractography-based bundle templates in a low-data setting.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Variational autoencoders for generating synthetic tractography-based bundle templates in a low-data setting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:39.022793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30dc3e1e-202a-495d-8424-de89aa1f4ef5 · outbound

This paper cites Deep metric learning using triplet network.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Deep metric learning using triplet network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:38.778976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a347adc7-9e8e-4897-b4d4-faca23f7a911 · outbound

This paper cites an unresolved cited work.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:23:38.557473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9099a2c4-dcb1-46e7-9189-efdceb533ef7 · outbound

This paper cites Design and validation of diffusion mri models of white matter.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Design and validation of diffusion mri models of white matter

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:38.330566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49443505-9987-4251-8fc9-bf12a44da5f5 · outbound

This paper cites Auto-encoding variational bayes, 2013.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Auto-encoding variational bayes, 2013

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:34.077736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c3cd460-cb64-4528-b035-5c0688ebc9e2 · outbound

This paper cites Trafic: fiber tract classification using deep learning.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Trafic: fiber tract classification using deep learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:38.121778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94c38509-4fb9-4caa-a21b-ce61b444c563 · outbound

This paper cites A unified approach to interpreting model predictions.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging A unified approach to interpreting model predictions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:34.275438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe9aa8ee-c5d8-418f-ac4e-d2812fc4e1fc · outbound

This paper cites Review of the development of multidimensional scaling methods.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Review of the development of multidimensional scaling methods

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:37.924627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 562f5bb0-44e6-498a-a089-fa873247e93a · outbound

This paper cites Men and women are different: diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Men and women are different: diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:37.664032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe5de628-a98b-4c9a-8196-8c775df6f558 · outbound

This paper cites Automatic tractography segmentation using a high-dimensional white matter atlas.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Automatic tractography segmentation using a high-dimensional white matter atlas

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:37.373327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 36058703-535c-4990-a73a-0702e3a59812 · outbound

This paper cites Unbiased groupwise registration of white matter tractography.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Unbiased groupwise registration of white matter tractography

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:37.153347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c6de3159-5e04-40b0-8c59-34b3a6ca8cf3 · outbound

This paper cites Pandala and Bruno Silva.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Pandala and Bruno Silva

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.886041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12497807-b009-4586-9761-a8fcbcf67c48 · outbound

This paper cites Integrated brain connectivity analysis with fmri, dti, and smri powered by interpretable graph neural networks.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Integrated brain connectivity analysis with fmri, dti, and smri powered by interpretable graph neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.768537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a3c1fd5-6a2b-43b3-868c-549368aaebdb · outbound

This paper cites Factorization machines.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Factorization machines

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.661575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 329d92de-86cf-4bea-a467-7faffb736658 · outbound

This paper cites Diffusion tensor imaging of the brain: review of clinical applications.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Diffusion tensor imaging of the brain: review of clinical applications

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.504620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8636119-77e2-4b1c-bf9d-aeb01159ab00 · outbound

This paper cites The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.373268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2bbaa2dd-298b-46ba-85b5-2c803bd0c91c · outbound

This paper cites Attention is all you need.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Attention is all you need

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:35.125932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f548c64a-56f9-44a2-92ba-279a510c3a8a · outbound

This paper cites Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:35.206212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fb5bacb-baf9-4ce7-864f-37a75f6513ef · outbound

This paper cites An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.218619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f63679f-70d6-42cb-8cf9-892c462cc707 · outbound

This paper cites Deep white matter analysis (deepwma): Fast and consistent tractography segmentation.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Deep white matter analysis (deepwma): Fast and consistent tractography segmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:36.032382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 022fba20-d643-43bc-a8ca-8fdfe027700f · outbound

This paper cites Disentangled and proportional representation learning for multi-view brain connectomes.

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging Disentangled and proportional representation learning for multi-view brain connectomes

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:23:35.905892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.