Paper Citation Record · LEDGER

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models

As of 7 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2602.15315.

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

pith.paper-citation-record.v1
2602.15315 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:59:24.671531Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T16:31:09.661787Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy0
unresolved13
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bea0e375-5c01-4798-8c99-b2c678e82931 · outbound

This paper cites Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:23.923891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0b2c317-277b-4bbe-be8f-d223ea39143a · outbound

This paper cites an unresolved cited work.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.564750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d510f30-e985-4ad3-a0d5-a85ffced5d49 · outbound

This paper cites URLhttps://doi.org/10.1007/ s10916-025-02272-2.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models URLhttps://doi.org/10.1007/ s10916-025-02272-2

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.363849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc6e2e3-f19e-4960-a41c-dd85655e7254 · outbound

This paper cites For WinCLIP, we strictly follow the official settings (Jeong et al., 2023), employing the ViT-B/16+ backbone with an input resolution of 240×240 and the standard prompt ensemble.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models For WinCLIP, we strictly follow the official settings (Jeong et al., 2023), employing the ViT-B/16+ backbone with an input resolution of 240×240 and the standard prompt ensemble

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.610044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b646f91-633b-4c37-93f2-6b11abbd690d · outbound

This paper cites Patient-level anomaly classification results obtained by aggregating slice-wise CLS-token anomaly scores (max over slices).

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Patient-level anomaly classification results obtained by aggregating slice-wise CLS-token anomaly scores (max over slices)

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.671531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b48ff423-99a3-4810-91ce-6cff9b340ac3 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.425063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c047fda-ad63-4121-83c2-0cd3271ed16e · outbound

This paper cites Kitamura, Sarthak Pati, Luciano M.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Kitamura, Sarthak Pati, Luciano M

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.075632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4139752c-1582-4f31-ac17-d35c6e95bd02 · outbound

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

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.147834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5885e0b-4b9e-4bdf-af8a-233714eaac55 · outbound

This paper cites Analysis of the miccai brain tumor segmentation–metastases (brats-mets) 2025 lighthouse challenge: Brain metastasis segmentation on pre-and post-treatment mri.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Analysis of the miccai brain tumor segmentation–metastases (brats-mets) 2025 lighthouse challenge: Brain metastasis segmentation on pre-and post-treatment mri

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.310167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7503b3b0-c3b6-471a-abb5-0e7e40733530 · outbound

This paper cites an unresolved cited work.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.487838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a53a8cac-f8af-4b9c-8c95-181ac35177c2 · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.192836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aaa937a-c8e2-450b-92c8-51d7715a9644 · outbound

This paper cites Unsupervised brain lesion segmentation from mri using a convolutional au- toencoder.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models Unsupervised brain lesion segmentation from mri using a convolutional au- toencoder

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.014056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40be77da-1961-48f0-a5d0-000f3cfadf23 · outbound

This paper cites doi: https://doi.org/10.

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models doi: https://doi.org/10

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-02T22:59:24.261026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4977e6f5-7fd6-4f1c-afd8-cee27dfdee7a · inbound

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms cites this paper.

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-14T16:31:09.661787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.