Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology | Paper Citation Record

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This paper cites Deep industrial image anomaly detection: A survey,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Deep industrial image anomaly detection: A survey,

Reference 1

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Observation e6f3f77d-a3b4-4e2f-9973-e75d44a46a92 · outbound

This paper cites Machine learning for anomaly detection: A systematic review,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Machine learning for anomaly detection: A systematic review,

Reference 2

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Observation 398381ee-ab62-4611-9a8e-804ac5bbadbc · outbound

This paper cites Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images,

Reference 3

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Observation 0282ece7-7aa8-4b59-b8a7-e5a4dba89927 · outbound

This paper cites Learning image representations for anomaly detection: application to discovery of histological alterations in drug development,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Learning image representations for anomaly detection: application to discovery of histological alterations in drug development,

Reference 4

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Observation f6b35fe9-e6f2-43ec-9aa4-bfb2dc269694 · outbound

This paper cites Automated anomaly detection in histology images using deep learning,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Automated anomaly detection in histology images using deep learning,

Reference 5

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Observation 1ace07b6-23d2-4a6a-8522-2562d877201a · outbound

This paper cites A survey on unsupervised anomaly detection algorithms for industrial images,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology A survey on unsupervised anomaly detection algorithms for industrial images,

Reference 6

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Observation fb3c09f8-c92a-4682-9b20-820db08f938d · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 7

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Observation f857d157-bca7-4454-82a2-6c7732d9b2ba · outbound

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

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Medianomaly: A comparative study of anomaly detection in medical images,

Reference 8

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Observation 6eac206e-ef00-4362-8018-47d2d60f5685 · outbound

This paper cites Unsupervised pathology detection: a deep dive into the state of the art,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Unsupervised pathology detection: a deep dive into the state of the art,

Reference 9

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Observation 7cc687f8-13c6-4051-b3b8-270d258712a5 · outbound

This paper cites Bmad: Benchmarks for medical anomaly detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Bmad: Benchmarks for medical anomaly detection,

Reference 10

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Observation 53bc383c-6d81-48d1-97b5-e16b589bb9ab · outbound

This paper cites A unified model for multi-class anomaly detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology A unified model for multi-class anomaly detection,

Reference 11

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Observation 8d9c3941-d3b6-4049-8c0d-2ebfdedc6479 · outbound

This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 12

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Observation dc602954-6eca-4f69-b82f-55853b0196d8 · outbound

This paper cites Deep one-class classifi- cation via interpolated gaussian descriptor,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Deep one-class classifi- cation via interpolated gaussian descriptor,

Reference 13

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Observation 6ae70bc9-33c9-4622-9e88-9d386455916b · outbound

This paper cites Denoising autoencoders for unsupervised anomaly detection in brain mri,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Denoising autoencoders for unsupervised anomaly detection in brain mri,

Reference 14

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Observation 96fde5f4-69a2-483b-8278-9654ba606d5d · outbound

This paper cites Constrained unsupervised anomaly segmentation,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Constrained unsupervised anomaly segmentation,

Reference 15

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Observation b1432ec4-b8e8-4238-99ab-0372897291de · outbound

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

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Ganomaly: Semi- supervised anomaly detection via adversarial training,

Reference 16

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Observation 2a1046d3-4315-4ec2-acf1-65e6d302cff0 · outbound

This paper cites Unsupervised anomaly localization with structural feature-autoencoders,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Unsupervised anomaly localization with structural feature-autoencoders,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:15.800835Z

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Observation 4d22d730-9193-4e16-9c37-32fa0c50fc26 · outbound

This paper cites DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 18

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Observation cef77813-6475-457e-ba63-2116b42e4bed · outbound

This paper cites Trans- former based models for unsupervised anomaly segmentation in brain mr images,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Trans- former based models for unsupervised anomaly segmentation in brain mr images,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:15.626799Z

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Observation 56241f9c-b960-4a5a-9c1b-b590bc5892ef · outbound

This paper cites Panda: Adapting pretrained features for anomaly detection and segmentation,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Panda: Adapting pretrained features for anomaly detection and segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:15.498448Z

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Observation 76b2afd5-4ddb-4883-9365-db35a98ef8ff · outbound

This paper cites Towards total recall in industrial anomaly detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Towards total recall in industrial anomaly detection,

Reference 21

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Observation 42f340f0-d472-4dd5-8239-6ac8e3e5488f · outbound

This paper cites Cfa: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Cfa: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:15.193787Z

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Observation 6b2e7ee2-529c-4f6b-99a9-d7e7455a7e41 · outbound

This paper cites DFKDE - Anomalib Documentation,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology DFKDE - Anomalib Documentation,

Reference 23

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Observation 6804b814-83ee-4485-870f-5c6538818245 · outbound

This paper cites Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

Reference 24

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Observation fa14b223-038e-489b-9247-99ed22cb20c2 · outbound

This paper cites Padim: a patch dis- tribution modeling framework for anomaly detection and localization,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Padim: a patch dis- tribution modeling framework for anomaly detection and localization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:14.787229Z

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Observation 9ebe4a64-1537-4f78-9a2d-1808d5364a27 · outbound

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

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Anomaly detection via reverse distillation from one-class embedding,

Reference 26

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Observation 67ea07d2-c8a1-48d7-9bce-23f36d549e49 · outbound

This paper cites Revisiting reverse distillation for anomaly detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Revisiting reverse distillation for anomaly detection,

Reference 27

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Observation 031d3db1-e059-47a5-8471-9e11712f13ac · outbound

This paper cites Student-Teacher Feature Pyramid Matching for Anomaly Detection.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Student-Teacher Feature Pyramid Matching for Anomaly Detection

Reference 28

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Observation 0d522c3a-d8f1-43f9-ac33-ef8e76033fed · outbound

This paper cites Recontrast: Domain-specific anomaly detection via contrastive reconstruction,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Recontrast: Domain-specific anomaly detection via contrastive reconstruction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:14.437613Z

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Observation 2f695de4-13b0-4895-b022-cd3a415239c8 · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 30

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Observation 171e16ef-0075-4ed0-945d-672b861c932a · outbound

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

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Cflow-ad: Real-time unsu- pervised anomaly detection with localization via conditional normalizing flows,

Reference 31

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Observation 7a6dbda3-254d-4481-b899-494baaecd693 · outbound

This paper cites Fully convo- lutional cross-scale-flows for image-based defect detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Fully convo- lutional cross-scale-flows for image-based defect detection,

Reference 32

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Observation 8f968e44-3854-4b50-a151-9d59c09505d5 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localization,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Cutpaste: Self-supervised learning for anomaly detection and localization,

Reference 33

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Observation 6d1cda12-f58d-41e7-9c2c-ee5d40b727ce · outbound

This paper cites Anomaly detection in medical imaging with deep perceptual autoen- coders,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Anomaly detection in medical imaging with deep perceptual autoen- coders,

Reference 34

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Observation bac2a3c3-dc79-4c53-afab-e4af9a6d1e91 · outbound

This paper cites Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology,

Reference 35

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Observation ec43cdd1-46d9-4163-9da1-12349518cb76 · outbound

This paper cites Unsupervised anomaly detection in digital pathology using gans,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Unsupervised anomaly detection in digital pathology using gans,

Reference 36

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Observation 59d0ac66-65fe-4c18-9f3d-0a2b52c19381 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Perceptual losses for real-time style transfer and super-resolution,

Reference 37

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Observation 44c08e30-0ce9-4b89-b999-df1bd8551901 · outbound

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

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Ganomaly: Semi-supervised anomaly detection via adversarial training,

Reference 38

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Observation 4dde7b0f-5331-4e15-844c-a8a4155d7572 · outbound

This paper cites Unsupervised anomaly detection on histopathology images using adversarial learning and simulated anomaly,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Unsupervised anomaly detection on histopathology images using adversarial learning and simulated anomaly,

Reference 39

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Observation ceda4458-a22c-437f-ae88-6ed99e1fd769 · outbound

This paper cites Diffusion models for out-of-distribution detection in digital pathology,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Diffusion models for out-of-distribution detection in digital pathology,

Reference 40

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Observation f0dd2897-6724-4f73-b5cc-c2a94282feb4 · outbound

This paper cites 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset,

Reference 41

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Observation e325a533-6671-460c-b064-0576b738277b · outbound

This paper cites Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:12.688242Z

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Observation 678796e2-3bdd-4eab-aebc-5e0b21564d9a · outbound

This paper cites A cross-platform informatics system for the gut cell atlas: integrating clinical, anatomical and histological data,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology A cross-platform informatics system for the gut cell atlas: integrating clinical, anatomical and histological data,

Reference 43

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Observation d049223e-39bf-4fcc-9be6-3669828bb4a1 · outbound

This paper cites Glo-in-one: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Glo-in-one: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:12.297030Z

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Observation 257c8338-65c7-4490-a20b-32efe1066930 · outbound

This paper cites Unitopatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Unitopatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:12.115050Z

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Observation 7e5459c4-da0c-407b-808a-903c1dcddc57 · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 46

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Observation e6c6417d-e1f3-4066-b6ef-4cb76e561242 · outbound

This paper cites Feasibility of universal anomaly detection without knowing the abnormality in medical images,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Feasibility of universal anomaly detection without knowing the abnormality in medical images,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:11.945133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d958326-7810-4426-9481-7d218a1c357e · outbound

This paper cites Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly de- tection,.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly de- tection,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:11.737906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers