Paper Citation Record · LEDGER

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks

As of 23 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 2 inbound Pith citation observations for arXiv:2501.18782.

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

pith.paper-citation-record.v1
2501.18782 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:31:24.298607Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:31:24.235524Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:20:15.355871Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3088ff1c-c179-4fcf-bde5-33b4c1b9cb4b · outbound

This paper cites PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbe38f81-4398-491f-b399-2ca2e895ea04 · outbound

This paper cites an unresolved cited work.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T22:31:24.506817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 10723a5a-a385-4556-9a2d-03ab156d32ca · outbound

This paper cites Dataset Of the 533 patients who provided images, 344 met criteria for inclusion and were advanced through screening.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Dataset Of the 533 patients who provided images, 344 met criteria for inclusion and were advanced through screening

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.497964Z

Source-reported events for the cited work

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Observation 8cd91e2d-a3ba-4103-bd1c-163f9817c34d · outbound

This paper cites Moreover, we also devise a novel regression activation map for inter- pretability by ranking attention scores.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Moreover, we also devise a novel regression activation map for inter- pretability by ranking attention scores

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.489514Z

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Observation 917d6c29-2b7e-4aca-9280-6d897958384e · outbound

This paper cites an unresolved cited work.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T22:31:24.481422Z

Source-reported events for the cited work

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Observation c9b0e62a-7a38-4c2d-ac5c-4558f6e53a82 · outbound

This paper cites an unresolved cited work.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-09T22:31:24.473969Z

Source-reported events for the cited work

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Observation a8a16a99-a255-495f-8970-39e6ede5cd9a · outbound

This paper cites an unresolved cited work.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-09T22:31:24.465493Z

Source-reported events for the cited work

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Observation a692cc58-b263-42a0-baa3-d148ed7c178b · outbound

This paper cites Dermatology in china,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Dermatology in china,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.457089Z

Source-reported events for the cited work

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Observation 076c1ae2-bb91-4f7e-84be-068a165613a2 · outbound

This paper cites Negative impact of comorbidities on all-cause mortality of patients with psoriasis is partially alleviated by biologic treatment: A real-world case-control study,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Negative impact of comorbidities on all-cause mortality of patients with psoriasis is partially alleviated by biologic treatment: A real-world case-control study,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.449241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b0705aa9-1149-478a-ab34-e6b841768b37 · outbound

This paper cites Psenet: Psoriasis severity evaluation net- work,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Psenet: Psoriasis severity evaluation net- work,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.439308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bf0c378c-0c85-482e-9e3c-ab57cda7317c · outbound

This paper cites Artificial intelligence–based psoria- sis severity assessment: Real-world study and applica- tion,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Artificial intelligence–based psoria- sis severity assessment: Real-world study and applica- tion,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.431268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dfe2d31e-ef52-486c-b3b6-201854fa9519 · outbound

This paper cites Image-based automated psoria- sis area severity index scoring by convolutional neural networks,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Image-based automated psoria- sis area severity index scoring by convolutional neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.423953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 60547281-d2c5-49ff-8995-6cfa490d55df · outbound

This paper cites 078 remote assessment of psoriasis sever- ity with ai-based automated classification,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks 078 remote assessment of psoriasis sever- ity with ai-based automated classification,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.415931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 364aead2-a5b3-4953-9dbe-368751ae7653 · outbound

This paper cites Deep learning-based psoriasis assess- ment: Harnessing clinical trial imaging for accurate pso- riasis area severity index prediction,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Deep learning-based psoriasis assess- ment: Harnessing clinical trial imaging for accurate pso- riasis area severity index prediction,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.407217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a396b746-0c43-44ba-805e-566dbf8c59bc · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Imagenet: A large-scale hierarchical image database,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.397249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 95d1c1e2-f0e6-43cc-952d-ed3dd88fc56c · outbound

This paper cites Automatic psoriasis lesion segmentation in two- dimensional skin images using multiscale superpixel clustering,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Automatic psoriasis lesion segmentation in two- dimensional skin images using multiscale superpixel clustering,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.388051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f38c0632-89b5-4c32-8bef-21f72dbda510 · outbound

This paper cites Evaluating psoriasis with psoriasis area and severity index, psori- asis global assessment, and lattice system physician’s global assessment,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Evaluating psoriasis with psoriasis area and severity index, psori- asis global assessment, and lattice system physician’s global assessment,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.377488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d9a39e1b-3a96-47c4-9b1f-782d072f4669 · outbound

This paper cites Intra-and interobserver variability of image- based pasi assessments in 120 patients suffering from plaque-type psoriasis,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Intra-and interobserver variability of image- based pasi assessments in 120 patients suffering from plaque-type psoriasis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.368261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b39f2272-4c69-486e-a2e3-6f1c8a10a75d · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Imagenet classification with deep convolutional neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.360159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 27cf9026-b5b3-4058-917e-fd2290d627e6 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Pytorch: An imperative style, high-performance deep learning library,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.351052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 83ce05f3-e6ac-430c-8580-49e9a5fb36a2 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Adam: A Method for Stochastic Optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T22:31:24.295608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b76a057a-18b8-47c4-9633-206791166aef · outbound

This paper cites Objective scoring of psoriasis area and severity index in 2d rgb images using deep learning,.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks Objective scoring of psoriasis area and severity index in 2d rgb images using deep learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:31:24.341614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 3088ff1c-c179-4fcf-bde5-33b4c1b9cb4b · inbound