Pith. sign in

Paper Citation Record · LEDGER

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2506.03420.

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

pith.paper-citation-record.v1
2506.03420 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:09:12.986403Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy17
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 43b1794e-6b96-4673-9cc6-2c7c71c4795a · outbound

This paper cites an unresolved cited work.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:09:18.214408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:10.872470Z digest=sha256:2f2c828cda156888fef1120f2ac8fea2884991bd2ee465330c52b18d5e39d2ba

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:18.102869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:10.944680Z digest=sha256:7b8bfd9e18ff89568c04b7d61d3fe559a2e75da6cf12411e57738556f2462645

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:17.921319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.057944Z digest=sha256:bf1f1d53ea3f43fb7720fb69ddb506b8a8f063fd3d2048042a69dd0c5f3662ef

Observation d75e75d5-838b-4af3-910d-b97dde86b043 · outbound

This paper cites A self-contrastive learning framework for skin cancer detection using histological images.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images A self-contrastive learning framework for skin cancer detection using histological images

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:17.781882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.198062Z digest=sha256:40aa112d02c00b9f0edf80c888d4d9290e042ca9eb4cdc5b24dfa7d61a9f2cf3

Observation aca4ae7e-1abc-457f-ae71-bcd1ef82dc41 · outbound

This paper cites Hi-mvit: A lightweight model for explainable skin disease classifi- cation based on modified mobilevit.Digital Health, 9: 20552076231207197, 2023.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Hi-mvit: A lightweight model for explainable skin disease classifi- cation based on modified mobilevit.Digital Health, 9: 20552076231207197, 2023

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:17.649654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.309647Z digest=sha256:3724cd33ee3ff7575bfa6e6c5c7f894a70bb2d02068e3425f88ba1a4e2e83fc0

Observation a7e88a63-f785-4a77-94d7-8b3bc1f64b29 · outbound

This paper cites Eva-02: A visual representation for neon genesis.Image and Vision Computing, 149:105171,.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Eva-02: A visual representation for neon genesis.Image and Vision Computing, 149:105171,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T11:09:11.426441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:09:11.426441Z digest=sha256:56dd154983a669f7727ede3e2060af321cc3e745a1618f8fe0bc4162652166e9

Observation 17adaf7e-876e-4b90-b3bd-9f00fda904dc · outbound

This paper cites an unresolved cited work.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:09:17.440658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.561761Z digest=sha256:701ee47477cbcbe6b582dfd37c5770b3565d9c29c50d78333b224d4d0b669b6a

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:17.286646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.705451Z digest=sha256:9e339462b7a01ea89c42c6df954801f55330708e968ee1bab7f0cf57322fe919

Observation cce497a6-dac9-468b-89ca-06cd0411981f · outbound

This paper cites Mini- mal sourced and lightweight federated transfer learning mod- els for skin cancer detection.Scientific Reports, 15:2605,.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Mini- mal sourced and lightweight federated transfer learning mod- els for skin cancer detection.Scientific Reports, 15:2605,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:17.132143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.855664Z digest=sha256:e7a40d6c274360f3938886dd9a50b39f22eec702240fe723ce469dc161182287

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:17.012294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:11.989058Z digest=sha256:2e6860a63390200344ecf631effb106b0ff018aacadfdc6793a0761901dd4094

Observation 59d17c5d-333c-4132-a8cc-a45d829ea8d4 · outbound

This paper cites Kurtansky, Brian M.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Kurtansky, Brian M

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:15.207617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.055702Z digest=sha256:dc65fffe11c882f1cf300d9c57b6d6de3891216554b3fbb5e185d5fc26165652

Observation f7b6258f-e6a7-4315-8454-35e6c4003ad8 · outbound

This paper cites A novel skin cancer assisted diagnosis method based on cap- sule networks with cbam.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images A novel skin cancer assisted diagnosis method based on cap- sule networks with cbam

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:14.748345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.127490Z digest=sha256:1a95875ebadcc4a9672da9075aa64668cd4675e5b589a0e2d4996f105bb3573a

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:14.644844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.197516Z digest=sha256:4d8c47a677f9b6eb982805a0556304dd55268833327882c34674731417bf1124

Observation 785a82bd-33b3-4256-bd49-9fc4a11ee2df · outbound

This paper cites Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications, 2022.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications, 2022

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:14.467027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.302287Z digest=sha256:27940694759116aa78b00ef44fe45b9ebe9d7b6378a58addb7a5667dfd207618

Observation c8eca13c-1e02-4afd-a2b7-39c1da2063cd · outbound

This paper cites an unresolved cited work.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:09:14.317804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.388550Z digest=sha256:2f3852ba089e4f85f6224c7257b481b6fc7378a345f1599852508ed95991d014

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:14.194903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.537989Z digest=sha256:4b0a3da891aa38de1773372d500b4a76fdb52148f29542cc94746e15944bcffd

Observation 5e28fa8e-0213-41fe-b81a-7a4984193478 · outbound

This paper cites Skin cancer detection using deep learning—a review.Diagnostics, 13(11):1911,.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Skin cancer detection using deep learning—a review.Diagnostics, 13(11):1911,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:14.007145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.640848Z digest=sha256:7052672763b33cdddd895e6ab319468d5503eaf9f495400b1399abffea0c30ea

Observation 4d5f8b71-a6c3-4919-9d25-b973731e6587 · outbound

This paper cites Garc´ıa-de-la Puente, Miguel L´opez-P´erez, La¨etitia Launet, and Valery Naranjo.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Garc´ıa-de-la Puente, Miguel L´opez-P´erez, La¨etitia Launet, and Valery Naranjo

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:13.887126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.716890Z digest=sha256:8a172cd133e03e641282e213bbe2e9be67d60afe0e6d6ea5f031e0d8391c0391

Observation 1cb49b28-1665-41a3-9ffe-d81a20457975 · outbound

This paper cites an unresolved cited work.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:09:13.746972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.776154Z digest=sha256:a1dd9280d1c1de71cff8bd98ea7d41c8fda34294be7ed0b314d1067acbf34d5a

Observation 7723ce4f-ae13-42eb-8604-cd67c2b3cfff · outbound

This paper cites Smart mobinet: A deep learning approach for accurate skin cancer diagnosis.Computers, Materials and Continua, 77(3):3533–3549, 2023.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Smart mobinet: A deep learning approach for accurate skin cancer diagnosis.Computers, Materials and Continua, 77(3):3533–3549, 2023

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:13.572144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.868560Z digest=sha256:a9691168c78ed03eb6ecb08076e53fb6df43fcf610644ee379c3c9347e6f9f08

Observation 84ba7183-027d-42df-8bee-503f00cf2753 · outbound

This paper cites Dscc net: Multi-classification deep learning models for diagnosing of skin cancer using dermoscopic images.Cancers, 15(7):2179,.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Dscc net: Multi-classification deep learning models for diagnosing of skin cancer using dermoscopic images.Cancers, 15(7):2179,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:13.451505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.936974Z digest=sha256:4e30cc7d67a2317ce3538ea26da32d2b4adf88fd2ea7fa9c48810fce4629971b

Observation 992043d9-9316-462f-871a-0ca9023ca988 · outbound

This paper cites Cbam: Convolutional block attention module, 2018.

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images Cbam: Convolutional block attention module, 2018

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:09:13.272306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:09:12.986403Z digest=sha256:64475c61b640853a71168456d80453ef71a110d6a89dc5c20ba7b8afa80f66e0

Pith citing papers

No inbound Pith citation observations are available.