Paper Citation Record · LEDGER

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2506.23209.

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

pith.paper-citation-record.v1
2506.23209 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:52:48.950232Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:52:47.511849Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:52:49.033713Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy16
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25841a21-72ef-4ccb-b666-b0dcda5adb4d · outbound

This paper cites Computed tomography (CT) imaging plays a key role in diagnosing appendicitis and re- lated conditions.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Computed tomography (CT) imaging plays a key role in diagnosing appendicitis and re- lated conditions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:53.003660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f59ea3e-2948-49c4-ba0d-d1c17d32a4d2 · outbound

This paper cites an unresolved cited work.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:52:52.812900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87095845-f1f1-482f-a07e-6f5529003ae6 · outbound

This paper cites an unresolved cited work.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:52:52.595154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91fd39e6-a872-4cb9-a6d7-53ec32e276f7 · outbound

This paper cites A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T21:52:49.107819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad093605-e721-4981-b778-b47d78a07af3 · outbound

This paper cites an unresolved cited work.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:52:52.405817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7ddfeae-04e8-4996-bd2a-bcac65796932 · outbound

This paper cites Total Loss Function The total loss function combines the losses from both the 2D and 3D tasks: Ltotal = α · Lapp + β · Ltype + γ · Lcenter + δ · Lcoherence + λ · L2D.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Total Loss Function The total loss function combines the losses from both the 2D and 3D tasks: Ltotal = α · Lapp + β · Ltype + γ · Lcenter + δ · Lcoherence + λ · L2D

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:52.192017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 009d5439-c06c-49cf-bd8d-0a2d5079bd7b · outbound

This paper cites Dataset and Model Implementation 3.1.1.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Dataset and Model Implementation 3.1.1

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:51.918485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec647feb-0552-4953-8735-4fb206fd784d · outbound

This paper cites Leveraging Slice Attention and external 2D datasets enhances small lesion classification, and integrating pre-trained 2D models improves accuracy and robustness.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Leveraging Slice Attention and external 2D datasets enhances small lesion classification, and integrating pre-trained 2D models improves accuracy and robustness

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:51.591467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 61f61e55-db08-415d-8070-15f926c086e3 · outbound

This paper cites Due to the retrospective nature of the study, informed consent was waived.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Due to the retrospective nature of the study, informed consent was waived

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:51.304856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation af369662-2377-4a47-adae-9ce2c2b0fcd4 · outbound

This paper cites Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:51.032356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 54f61d09-9797-4e83-89ed-afc7dcb74b63 · outbound

This paper cites The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:48.152679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95049f2e-c332-4c1b-af15-376f06457ac0 · outbound

This paper cites Crowdsourcing pneumotho- rax annotations using machine learning annotations on the nih chest x-ray dataset,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Crowdsourcing pneumotho- rax annotations using machine learning annotations on the nih chest x-ray dataset,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:50.753268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2fc39701-c484-49ee-94c5-04f0445d7acb · outbound

This paper cites Appendixnet: deep learning for diag- nosis of appendicitis from a small dataset of ct exams using video pretraining,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Appendixnet: deep learning for diag- nosis of appendicitis from a small dataset of ct exams using video pretraining,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:50.476588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f6131c6-a6d3-40a4-9ff2-188038a79cf5 · outbound

This paper cites Convolutional-neural-network-based diagnosis of appendicitis via ct scans in patients with acute ab- dominal pain presenting in the emergency department,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Convolutional-neural-network-based diagnosis of appendicitis via ct scans in patients with acute ab- dominal pain presenting in the emergency department,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:50.295718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 261d1965-0005-4151-99d7-fe720d74ae6a · outbound

This paper cites an unresolved cited work.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:52:50.074315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 212d8f4e-95ce-4ce3-9246-d54ee36b2839 · outbound

This paper cites Boosting breast ultrasound video classification by the guidance of keyframe fea- ture centers,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Boosting breast ultrasound video classification by the guidance of keyframe fea- ture centers,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:50.005649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e6bf305-f04f-43fb-ac7c-e2f32c39d16a · outbound

This paper cites A survey of hi- erarchical classification across different application do- mains,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans A survey of hi- erarchical classification across different application do- mains,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:49.865201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a07aa134-e25e-4b36-8782-307a82fc9b58 · outbound

This paper cites Holis- tic and comprehensive annotation of clinically signifi- cant findings on diverse ct images: learning from radi- ology reports and label ontology,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Holis- tic and comprehensive annotation of clinically signifi- cant findings on diverse ct images: learning from radi- ology reports and label ontology,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:49.731299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d4be3ee8-6d2d-4894-abe6-df33034ea6e7 · outbound

This paper cites Learning hierarchical attention for weakly-supervised chest x-ray abnormality localization and diagnosis,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Learning hierarchical attention for weakly-supervised chest x-ray abnormality localization and diagnosis,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:49.625732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa0a4b50-d262-4447-b279-eaba07030a43 · outbound

This paper cites Radimagenet: An open radio- logic deep learning research dataset for effective transfer learning,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Radimagenet: An open radio- logic deep learning research dataset for effective transfer learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:49.483997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 821571a1-cfc1-45eb-9372-9b73719b5dfb · outbound

This paper cites Swin transformer: Hierarchical vision transformer us- ing shifted windows,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Swin transformer: Hierarchical vision transformer us- ing shifted windows,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:49.361863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83f48afe-a015-464b-8ea9-38413eabbe5c · outbound

This paper cites Aocr2024 ai challenge,.

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans Aocr2024 ai challenge,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:49.263232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 91fd39e6-a872-4cb9-a6d7-53ec32e276f7 · inbound