Paper Citation Record · LEDGER

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.22762.

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

pith.paper-citation-record.v1
2505.22762 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:06:14.252707Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

30 of 30 outbound references displayed

verified exact1
verified fuzzy19
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a7fed61d-21a5-43b6-a2ca-1e98c712a0e5 · outbound

This paper cites Segment Any Anomaly without Training via Hybrid Prompt Regularization.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:10.657850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98cf52db-c032-429a-a354-7477ea60f36e · outbound

This paper cites Towards accurate unified anomaly segmentation.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Towards accurate unified anomaly segmentation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:18.268524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d99b3b06-8d73-40c7-9136-30c887b53b0c · outbound

This paper cites Autoencoders for unsupervised anomaly segmentation in brain mr images: a comparative study.Medical image analysis, 69:101952, 2021.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Autoencoders for unsupervised anomaly segmentation in brain mr images: a comparative study.Medical image analysis, 69:101952, 2021

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:18.118167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 46faf65b-38ea-42e8-825f-3c586c32c6c5 · outbound

This paper cites Diffusion models with implicit guidanceformedicalanomalydetection.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Diffusion models with implicit guidanceformedicalanomalydetection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:17.934325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bbbf7f33-4576-4a40-b664-00aaf9d36555 · outbound

This paper cites Denoising diffusion models for anomaly localization in medical images.arXiv preprint arXiv:2410.23834, 2024.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Denoising diffusion models for anomaly localization in medical images.arXiv preprint arXiv:2410.23834, 2024

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-07T13:06:14.499126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f2eb1e6e-c4a1-4efe-a987-7a95c83a18e0 · outbound

This paper cites Bmad: Benchmarks for medical anomaly detection.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Bmad: Benchmarks for medical anomaly detection

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:11.039627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9c8a04f-67a1-4c2f-ac02-412777328c6a · outbound

This paper cites Leveraging the mahalanobis distance to enhance unsupervised brain mri anomaly detection.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Leveraging the mahalanobis distance to enhance unsupervised brain mri anomaly detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:17.791011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f303758-d972-439d-a58f-ba3a3ed89c0a · outbound

This paper cites Loris-weakly-supervised anomaly detection for ultrasound images.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Loris-weakly-supervised anomaly detection for ultrasound images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:17.621064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8fcf4c2f-2518-4c3f-9f21-2843df2fd514 · outbound

This paper cites Towards total recall in industrial anomaly detection.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Towards total recall in industrial anomaly detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:11.272388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a788ddf4-6b05-4d5c-a06a-35f45f4f05e8 · outbound

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

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:11.326435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d209ebc-8d83-4239-80a5-96cc035935a9 · outbound

This paper cites Clipsam: Clipandsamcollaboration for zero-shot anomaly segmentation.Neurocomputing, 618:129122, 2025.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Clipsam: Clipandsamcollaboration for zero-shot anomaly segmentation.Neurocomputing, 618:129122, 2025

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:17.442211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4f041bea-b5d3-48e0-841f-57835440ec52 · outbound

This paper cites Fade: Few-shot/zero-shot anomaly detection engine using large vision-language model.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Fade: Few-shot/zero-shot anomaly detection engine using large vision-language model

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:17.269956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd15803e-7299-48bb-809d-8bb79f63ad1b · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detectionandlocalization.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Cutpaste: Self-supervised learning for anomaly detectionandlocalization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:17.111021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1f5d7e1-f3fa-4c52-bce5-7ba296529df4 · outbound

This paper cites Simplenet: A simple network for image anomaly de- tectionandlocalization.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Simplenet: A simple network for image anomaly de- tectionandlocalization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:16.967983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4451cc5-8c72-4ea3-b9ba-367a01cda0e8 · outbound

This paper cites Recontrast: Domain-specific anomaly detection via contrastive reconstruction.Advances in Neural Information Processing Systems, 36, 2024.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Recontrast: Domain-specific anomaly detection via contrastive reconstruction.Advances in Neural Information Processing Systems, 36, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:16.764381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b2887fbc-45d6-4afd-82f6-93f9a72f4f8f · outbound

This paper cites Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization.IEEE Access, 10:78446–78454, 2022.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization.IEEE Access, 10:78446–78454, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:11.776396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95c2dc05-c4b0-4d14-975b-3e4f61062b11 · outbound

This paper cites Anomalydetectionviareversedistillationfromone-classembedding.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Anomalydetectionviareversedistillationfromone-classembedding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:16.604028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 16c3b7ad-3eb4-4924-850a-29a96efd58e9 · outbound

This paper cites Cflow-ad: Real-time unsupervised anomaly detection with localizationviaconditionalnormalizingflows.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Cflow-ad: Real-time unsupervised anomaly detection with localizationviaconditionalnormalizingflows

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:16.474629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b9d2a14-2a64-4628-a0c0-1bcd854e0125 · outbound

This paper cites an unresolved cited work.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:06:16.243205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 889afe12-7a53-46ba-9768-528669bd0b12 · outbound

This paper cites Learning transferable visual models from natural language supervision.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Learning transferable visual models from natural language supervision

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:12.458238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c572a02-7786-4849-94c3-5b91611c2705 · outbound

This paper cites Winclip: Zero-/few-shotanomalyclassificationandsegmentation.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Winclip: Zero-/few-shotanomalyclassificationandsegmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:16.054498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b32cddb-e054-4a1b-8b17-8402b04b6510 · outbound

This paper cites Promptad: Zero-shot anomaly detection using text prompts.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Promptad: Zero-shot anomaly detection using text prompts

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:15.842829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1464c432-8b88-4879-a38b-729ed4b04752 · outbound

This paper cites Adaclip: Adaptingclipwithhybridlearnablepromptsforzero-shotanomalydetection.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Adaclip: Adaptingclipwithhybridlearnablepromptsforzero-shotanomalydetection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:15.602351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e042e026-fc55-4275-b9fc-81e07a93a983 · outbound

This paper cites Position-guided prompt learning for anomaly detection in chest x-rays.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Position-guided prompt learning for anomaly detection in chest x-rays

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:15.329312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 49e8d029-4341-421e-a563-fba0348e255d · outbound

This paper cites The Faiss library.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding The Faiss library

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:13.767878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 062963fa-b82e-4e2e-80b4-638d9283bbac · outbound

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

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:13.892624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0d6837a-0cbe-4144-88bc-abd8280967cf · outbound

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

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding The liver tumor segmentation benchmark (lits).Medical Image Analysis, 84:102680, 2023

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:14.006217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a85962c1-d0c2-4a1a-882a-a7dfedc91cab · outbound

This paper cites Miccai multi-atlas labelingbeyondthecranialvault–workshopandchallenge.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Miccai multi-atlas labelingbeyondthecranialvault–workshopandchallenge

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:15.088344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d07b773b-b6b1-42c4-87c0-0acec7dce523 · outbound

This paper cites Automated segmentation of macular edema in oct using deep neural networks.Medical image analysis, 55:216–227, 2019.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Automated segmentation of macular edema in oct using deep neural networks.Medical image analysis, 55:216–227, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:14.857721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f6e0d71-85db-490b-8e98-855288b13ff3 · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1):654, 2024.

MIAS-SAM: Medical Image Anomaly Segmentation without thresholding Segment anything in medical images.Nature Communications, 15(1):654, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:14.701925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.