CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection | Paper Citation Record

Observation 870a8561-9500-44e1-be3d-f01238d1b3df · outbound

This paper cites Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.NPJ digital medicine, 4(1):65, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.NPJ digital medicine, 4(1):65, 2021

Reference 1

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Observation a05593d6-04d5-460d-967d-3a37249462b5 · outbound

This paper cites Dual-path frequency discriminators for few-shot anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Dual-path frequency discriminators for few-shot anomaly detection

Reference 2

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Observation 4d60a985-fdff-47bb-be5a-0a43d37a4324 · outbound

This paper cites The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification, 2021

Reference 3

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Observation 5455c86c-a1ec-43ea-8832-e9ff0696c9bc · outbound

This paper cites Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features.Scientific Data, 4(1):170117, 2017.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features.Scientific Data, 4(1):170117, 2017

Reference 4

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Observation 28231fc6-cf06-4de4-9993-e0fdc8f5253c · outbound

This paper cites Bmad: Benchmarks for medical anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Bmad: Benchmarks for medical anomaly detection

Reference 5

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Observation cc183bad-8875-442b-934b-cede5738e0db · outbound

This paper cites Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:16.943653Z

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Observation 6cddb086-7c23-43be-8c13-cd9bb65a8e2b · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:17.029668Z

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Observation 73d5f09a-53ca-4a22-925b-dd74fc6fa0ef · outbound

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

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection The livesr tumor segmentation benchmark (lits).Medical Image Analysis, 84:102680, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:17.039582Z

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Observation c4a74862-1f8a-42d5-b2de-9aed738b4c7f · outbound

This paper cites Informative knowledge distillation for image anomaly segmentation.Knowledge-Based Systems, 248:108846, 2022.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Informative knowledge distillation for image anomaly segmentation.Knowledge-Based Systems, 248:108846, 2022

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:17.160834Z

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Observation 429541d5-67b1-4505-8c4b-266bc5ca0614 · outbound

This paper cites Recent advances and clinical applications of deep learning in medical image analysis.Medical image analysis, 79:102444, 2022.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Recent advances and clinical applications of deep learning in medical image analysis.Medical image analysis, 79:102444, 2022

Reference 10

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Observation 5ccdbdf7-89da-41af-9d9c-24f0ffc622d7 · outbound

This paper cites Unsu- pervised anomaly detection using style distillation.IEEE Access, 8:221494–221502, 2020.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Unsu- pervised anomaly detection using style distillation.IEEE Access, 8:221494–221502, 2020

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:17.350551Z

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Observation 2ee6d4eb-c420-4eb5-81e7-dc698c5c3ac8 · outbound

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

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Anomaly detection via reverse distillation from one- class embedding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:17.496559Z

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Observation 9cef6db7-91fd-4173-bc5a-7498e0f4ccc7 · outbound

This paper cites Diagnostic assessment of deep learning algo- rithms for detection of lymph node metastases in women with breast cancer.JAMA, 318(22):2199–2210, 12 2017.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Diagnostic assessment of deep learning algo- rithms for detection of lymph node metastases in women with breast cancer.JAMA, 318(22):2199–2210, 12 2017

Reference 13

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Observation 496dee74-a68d-48a2-b11a-8d8428a35e86 · outbound

This paper cites Deep learning for medical anomaly detection–a survey.ACM Computing Surveys (CSUR), 54(7):1–37, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Deep learning for medical anomaly detection–a survey.ACM Computing Surveys (CSUR), 54(7):1–37, 2021

Reference 14

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Observation e080b5d0-22d9-44bb-9c05-2f1156436fec · outbound

This paper cites Knowledge distil- lation: A survey.International Journal of Computer Vision, 129(6):1789–1819, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Knowledge distil- lation: A survey.International Journal of Computer Vision, 129(6):1789–1819, 2021

Reference 15

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Observation ea0dff5f-9b5e-44c5-a0b8-f9170d664f09 · outbound

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

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Cflow-ad: Real-time unsu- pervised anomaly detection with localization via conditional normalizing flows

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:17.932613Z

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Observation 8957b3e1-cbea-4b6b-a131-80ba5b696adc · outbound

This paper cites Recontrast: Domain- specific anomaly detection via contrastive reconstruction.Advances in Neural Infor- mation Processing Systems, 36:10721–10740, 2023.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Recontrast: Domain- specific anomaly detection via contrastive reconstruction.Advances in Neural Infor- mation Processing Systems, 36:10721–10740, 2023

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:18.080107Z

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Observation 923942e1-ab45-42ab-a3e3-23d401675c23 · outbound

This paper cites A diffusion-based framework for multi- class anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection A diffusion-based framework for multi- class anomaly detection

Reference 18

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Observation 83574cb9-f39e-4f8c-9269-4a2657f0021e · outbound

This paper cites Fusing multispectral information for retinal layer segmentation.npj Digital Medicine, 8(1):39, 2025.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Fusing multispectral information for retinal layer segmentation.npj Digital Medicine, 8(1):39, 2025

Reference 19

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Observation 49206dce-2ba9-427d-ad6d-c09ee020bc48 · outbound

This paper cites A semantic-enhanced method based on deep svdd for pixel-wise anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection A semantic-enhanced method based on deep svdd for pixel-wise anomaly detection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:18.348406Z

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Observation d065524e-2f91-413f-9dd2-c5a39d7e26d6 · outbound

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

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Automated segmentation of macular edema in oct using deep neural networks.Medical Image Analysis, 55:216–227, 2019

Reference 21

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Observation e6f00e9b-c5d4-41d1-85a5-ae2a80d93172 · outbound

This paper cites Adapting visual-language models for generalizable anomaly detection in med- ical images.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Adapting visual-language models for generalizable anomaly detection in med- ical images

Reference 22

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Observation 61de63e9-f340-4436-bec7-58a610d7b6b1 · outbound

This paper cites an unresolved cited work.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Unresolved cited work

Reference 23

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Observation 913b321d-27d0-4143-809c-a553651d0252 · outbound

This paper cites Multi-scale feature reconstruction network for industrial anomaly de- tection.Knowledge-Based Systems, 305:112650, 2024.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Multi-scale feature reconstruction network for industrial anomaly de- tection.Knowledge-Based Systems, 305:112650, 2024

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:18.897469Z

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Observation 10e91cf6-07b0-4014-b227-c71050071aa5 · outbound

This paper cites Aptos 2019 blindness detection.https: //kaggle.com/competitions/aptos2019-blindness-detection,.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Aptos 2019 blindness detection.https: //kaggle.com/competitions/aptos2019-blindness-detection,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:19.045715Z

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Observation 834d0056-030a-41be-9288-29c875f619e7 · outbound

This paper cites Kermany and et al.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Kermany and et al

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Observation 5fd6bf39-2412-4ecd-bbd4-dab479d08d66 · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 27

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Observation 3212597a-5947-47f3-a6ab-e027d2c228e8 · outbound

This paper cites Multimodal industrial anomaly detection via geometric prior.IEEE Transactions on Circuits and Systems for Video Technology, pages 1–1, 2025.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Multimodal industrial anomaly detection via geometric prior.IEEE Transactions on Circuits and Systems for Video Technology, pages 1–1, 2025

Reference 28

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Observation 227e0b22-5123-42c6-90d9-d501e4f0a0fa · outbound

This paper cites A survey on deep learning in medical image analysis.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection A survey on deep learning in medical image analysis

Reference 29

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Observation b8296097-cf34-43ad-bd01-9eb276d3d6f1 · outbound

This paper cites Dl- sanet: A dual-path learnable structure-prior attention network for retinal layer seg- mentation.Biomedical Signal Processing and Control, 121:110250, 2026.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Dl- sanet: A dual-path learnable structure-prior attention network for retinal layer seg- mentation.Biomedical Signal Processing and Control, 121:110250, 2026

Reference 30

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Observation 0f41e0fa-8d4d-435a-a32f-b44596cb1cab · outbound

This paper cites Unlocking the potential of reverse distillation for anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Unlocking the potential of reverse distillation for anomaly detection

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Observation b66ae8bc-90c6-4df5-81ac-e2a093863818 · outbound

This paper cites Simplenet: A simple net- work for image anomaly detection and localization.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Simplenet: A simple net- work for image anomaly detection and localization

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Observation 7bbed7f0-8322-42bc-867c-969503810070 · outbound

This paper cites an unresolved cited work.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Unresolved cited work

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Observation 8e412957-3f85-4d8d-8e3e-05113f3a7499 · outbound

This paper cites Integrating local and global correlations with mamba-transformer for multi-class anomaly detection.Knowledge- Based Systems, 324:113740, 2025.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Integrating local and global correlations with mamba-transformer for multi-class anomaly detection.Knowledge- Based Systems, 324:113740, 2025

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Observation ae1e8517-ee3a-4661-abdd-0dc2fd293f71 · outbound

This paper cites Mocca: Multilayer one-class classification for anomaly detection.IEEE transactions on neural networks and learning systems, 33 (6):2313–2323, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Mocca: Multilayer one-class classification for anomaly detection.IEEE transactions on neural networks and learning systems, 33 (6):2313–2323, 2021

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:20.482897Z

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Observation 5388e7f9-cbb7-4483-83cf-fa1abd30881d · outbound

This paper cites Menze, Andras Jakab, and et al.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Menze, Andras Jakab, and et al

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Observation 8701d2c5-e2a5-4607-88c5-d2e1646622e2 · outbound

This paper cites Few-shot medical anomaly detection through centroid consultation back and test-time self-calibration.Pattern Recognition, page 113261, 2026.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Few-shot medical anomaly detection through centroid consultation back and test-time self-calibration.Pattern Recognition, page 113261, 2026

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Observation 54ee0961-27c4-458b-bd2a-9fe321759a1d · outbound

This paper cites Towards total recall in industrial anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Towards total recall in industrial anomaly detection

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Observation a8244441-4f02-4ae2-8b41-dcff7b97a3f8 · outbound

This paper cites Asymmet- ric student-teacher networks for industrial anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Asymmet- ric student-teacher networks for industrial anomaly detection

Reference 39

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Observation 2b9a9827-9ad6-4c26-88a1-c6ae068edb88 · outbound

This paper cites Multireso- lution knowledge distillation for anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Multireso- lution knowledge distillation for anomaly detection

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Observation ae3cb79f-5e5a-49ff-9921-330c336f8553 · outbound

This paper cites Deep learning in medical image analysis.Annual review of biomedical engineering, 19(1):221–248, 2017.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Deep learning in medical image analysis.Annual review of biomedical engineering, 19(1):221–248, 2017

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Observation 056b89e1-c0c2-4021-80e8-8eaf63f6ac73 · outbound

This paper cites Learning and Evaluating Representations for Deep One-class Classification.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Learning and Evaluating Representations for Deep One-class Classification

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Observation 4748f0b1-c6f1-4630-b5dd-dbed04e2690b · outbound

This paper cites AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning

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Observation 0ed0e738-6452-4fc0-a410-8cbe8065c280 · outbound

This paper cites Does knowledge distillation really work?Advances in neural in- formation processing systems, 34:6906–6919, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Does knowledge distillation really work?Advances in neural in- formation processing systems, 34:6906–6919, 2021

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Observation 89cd667b-d9c8-4198-88dc-d8337297ea02 · outbound

This paper cites Unsupervised Visual Defect Detection with Score-Based Generative Model.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Unsupervised Visual Defect Detection with Score-Based Generative Model

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Observation bbf3321d-5b2a-4a1c-93a5-172ceab1a30c · outbound

This paper cites Two-stage reverse knowledge distilla- tion incorporated and self-supervised masking strategy for industrial anomaly detec- tion.Knowledge-Based Systems, 273:110611, 2023.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Two-stage reverse knowledge distilla- tion incorporated and self-supervised masking strategy for industrial anomaly detec- tion.Knowledge-Based Systems, 273:110611, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:21.714247Z

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Observation c4d4d50e-0b20-4bbd-8663-9b48a1d768bb · outbound

This paper cites Anomaly detection in medi- cal imaging-a mini review.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Anomaly detection in medi- cal imaging-a mini review

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Observation 2862719d-0323-4935-b3d2-2cd5c974315a · outbound

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

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Student-Teacher Feature Pyramid Matching for Anomaly Detection

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Observation ea4eead5-5e8c-4d5d-a49d-d62bd5f4ce7d · outbound

This paper cites Tinyvit: Fast pretraining distillation for small vision transformers.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Tinyvit: Fast pretraining distillation for small vision transformers

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Observation 702bd9dd-a430-445b-8bfb-45e12c1aed67 · outbound

This paper cites Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise

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Observation 7d0f00a9-4ee5-41ff-8589-5d22dec64454 · outbound

This paper cites Normal image guided segmen- tation framework for unsupervised anomaly detection.IEEE Transactions on Circuits and Systems for Video Technology, 34(6):4639–4652, 2024.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Normal image guided segmen- tation framework for unsupervised anomaly detection.IEEE Transactions on Circuits and Systems for Video Technology, 34(6):4639–4652, 2024

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Observation 9d4a03d5-795f-4690-9915-bd6f522f556e · outbound

This paper cites Beyond feature mapping: Dual-heterogeneous knowledge distillation with mamba for industrial anomaly detection.Expert Systems with Applications, page 131146, 2026.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Beyond feature mapping: Dual-heterogeneous knowledge distillation with mamba for industrial anomaly detection.Expert Systems with Applications, page 131146, 2026

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Observation 390d9549-5937-4988-acc6-84d3be4881a6 · outbound

This paper cites Reconstructed student-teacher and discriminative networks for anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Reconstructed student-teacher and discriminative networks for anomaly detection

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Observation 1f7e0753-d720-4936-851d-b01e2296eb76 · outbound

This paper cites Learning semantic context from normal samples for unsupervised anomaly detection.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Learning semantic context from normal samples for unsupervised anomaly detection

Reference 54

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Observation df471489-f666-4aae-b84f-006fe86b1c36 · outbound

This paper cites Self-supervised learning for anomaly detection with dynamic local augmentation.IEEE Access, 9: 147201–147211, 2021.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Self-supervised learning for anomaly detection with dynamic local augmentation.IEEE Access, 9: 147201–147211, 2021

Reference 55

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Observation f8ddebef-b238-4dc0-92da-f55a3dfb0599 · outbound

This paper cites Msflow: Multiscale flow-based framework for unsupervised anomaly detection.IEEE Transac- tions on Neural Networks and Learning Systems, 2024.

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection Msflow: Multiscale flow-based framework for unsupervised anomaly detection.IEEE Transac- tions on Neural Networks and Learning Systems, 2024

Reference 56

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