Paper Citation Record · LEDGER

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.18481.

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

pith.paper-citation-record.v1
2507.18481 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:37:13.205036Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

27 of 27 outbound references displayed

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unresolved27
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External citation measurements

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Outbound references

Observation ca1b5574-c744-4934-8cf3-0c897b0441b4 · outbound

This paper cites Ganomaly: Semi-supervised anomaly detection via adversarial training.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Ganomaly: Semi-supervised anomaly detection via adversarial training

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.297307Z

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Observation f620eca7-9cd8-4336-a80a-37979e78c14d · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.321454Z

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Observation bb224f31-8927-42d5-9628-045840a66b36 · outbound

This paper cites Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features.Scientific data, 4(1): 1–13, 2017.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features.Scientific data, 4(1): 1–13, 2017

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.333083Z

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Observation 514c9cbf-0060-40bd-a237-a4f61ab1ff89 · outbound

This paper cites Bmad: Benchmarks for medical anomaly detection.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Bmad: Benchmarks for medical anomaly detection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.372844Z

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Observation effbfb9b-a2c2-4d2e-a7c3-bff67b9a2ee3 · outbound

This paper cites Improving unsupervised defect seg- mentation by applying structural similarity to autoencoders.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Improving unsupervised defect seg- mentation by applying structural similarity to autoencoders

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.403074Z

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Observation 2b8a42ee-1cb9-44e0-9d84-8b3abc005954 · outbound

This paper cites The Liver Tumor Segmentation Benchmark (LiTS).

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection The Liver Tumor Segmentation Benchmark (LiTS)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.415825Z

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Observation 1cbb56b4-f1e9-4d8a-bc68-6a31e5ac6693 · outbound

This paper cites The liver tumor segmentation benchmark (lits).Medical Image Analysis, 84:102680, 2023.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection The liver tumor segmentation benchmark (lits).Medical Image Analysis, 84:102680, 2023

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.420397Z

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Observation 83387922-530f-4dd7-a34e-e4109bbd14cf · outbound

This paper cites Rethinking Au- toencoders for Medical Anomaly Detection from A Theoreti- cal Perspective.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Rethinking Au- toencoders for Medical Anomaly Detection from A Theoreti- cal Perspective

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.436907Z

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Observation d4245091-04cb-4a2d-88a5-eab40be43124 · outbound

This paper cites MedIAnomaly: A comparative study of anomaly detection in medical images.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection MedIAnomaly: A comparative study of anomaly detection in medical images

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.454747Z

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Observation b6e17662-e9d5-4bef-a2c4-b85aa0f2acae · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Emerg- ing properties in self-supervised vision transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.511632Z

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Observation d302018b-6bec-4b97-8b2a-e78eea5c1545 · outbound

This paper cites Utrad: Anomaly detection and localization with u-transformer.Neural Networks, 147:53–62, 2022.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Utrad: Anomaly detection and localization with u-transformer.Neural Networks, 147:53–62, 2022

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.544748Z

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Observation cc281f81-86bf-4bc7-945a-9f26330ffd12 · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.594749Z

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Observation d6b0a476-beba-4cf0-86ad-d20b5795e2d4 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.632026Z

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Observation 5dcf8d9f-9170-4f9a-a935-5ba432392aa5 · outbound

This paper cites Anomaly Detection via Re- verse Distillation from One-Class Embedding.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Anomaly Detection via Re- verse Distillation from One-Class Embedding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.674746Z

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Observation 556c74e6-01d4-4a5f-bcbb-7104483d012c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.724751Z

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Observation 9d61a704-8b7b-4aff-9f37-54a1f295324b · outbound

This paper cites Zero-shot out-of-distribution detection based on the pre-trained model clip.Proceedings of the AAAI Conference on Artificial Intelligence, 36(6):6568–6576, 2022.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Zero-shot out-of-distribution detection based on the pre-trained model clip.Proceedings of the AAAI Conference on Artificial Intelligence, 36(6):6568–6576, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.765577Z

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Observation 776c26ed-b440-4daf-984a-fe7c3f1ef0ff · outbound

This paper cites Masked Autoencoders for Un- supervised Anomaly Detection in Medical Images.Procedia Computer Science, 225:969–978, 2023.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Masked Autoencoders for Un- supervised Anomaly Detection in Medical Images.Procedia Computer Science, 225:969–978, 2023

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.815756Z

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Observation 01baade6-2c0a-4c6f-bed0-b93c9a88045a · outbound

This paper cites Anomalygpt: Detecting in- dustrial anomalies using large vision-language models.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Anomalygpt: Detecting in- dustrial anomalies using large vision-language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.844752Z

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Observation 7d9759a0-f403-4ffa-b5b1-50b1c57d975a · outbound

This paper cites Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.874745Z

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Observation 4de117ee-63ca-42ec-9d6e-2944b301db77 · outbound

This paper cites ReContrast: domain-specific anomaly detection via con- trastive reconstruction.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection ReContrast: domain-specific anomaly detection via con- trastive reconstruction

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.920778Z

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Observation f2dd059c-dc10-4178-90b5-41ab06e2ce71 · outbound

This paper cites Encoder-Decoder Contrast for Unsupervised Anomaly De- tection in Medical Images.IEEE Transactions on Medical Imaging, 43(3):1102–1112, 2024.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Encoder-Decoder Contrast for Unsupervised Anomaly De- tection in Medical Images.IEEE Transactions on Medical Imaging, 43(3):1102–1112, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:12.962672Z

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Observation fde10658-593d-4812-8569-5df2c282fff2 · outbound

This paper cites Girshick.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Girshick

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:13.007578Z

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Unavailable: canonical work link unavailable.

Observation cf64fe89-4183-4018-8d61-9fe7afb1cc94 · outbound

This paper cites Automated seg- mentation of macular edema in oct using deep neural net- works.Medical image analysis, 55:216–227, 2019.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Automated seg- mentation of macular edema in oct using deep neural net- works.Medical image analysis, 55:216–227, 2019

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:13.042299Z

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Observation 2918a550-6254-4a4c-ac06-3696f6703f6f · outbound

This paper cites Self-supervised masking for unsupervised 9 anomaly detection and localization.IEEE Transactions on Multimedia, 2022.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Self-supervised masking for unsupervised 9 anomaly detection and localization.IEEE Transactions on Multimedia, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:13.104750Z

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Observation 6fe3c438-8ceb-431a-a5d9-32d6b6fd2c6f · outbound

This paper cites an unresolved cited work.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:13.145554Z

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Observation 548a6452-45e9-41c4-8a99-e9f552815f44 · outbound

This paper cites Winclip: Zero- /few-shot anomaly classification and segmentation.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19606–19616, 2023.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Winclip: Zero- /few-shot anomaly classification and segmentation.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19606–19616, 2023

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:13.191413Z

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Observation dc12de16-4c99-481b-a9ec-0a6ffcc701df · outbound

This paper cites Percep- tual losses for real-time style transfer and super-resolution.

Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Percep- tual losses for real-time style transfer and super-resolution

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:13.205036Z

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Pith citing papers

No inbound Pith citation observations are available.