Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T05:19:54.101363Z
Paper Citation Record · LEDGER
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes
As of 22 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2608.05101.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
48 of 48 outbound references displayed
- verified exact0
- verified fuzzy41
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 77f1b354-8e5c-4f39-8dea-b11516223037 · outbound
This paper cites Toward medical deepfake detection: A comprehensive dataset and novel method.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Toward medical deepfake detection: A comprehensive dataset and novel method
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:01.180958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb70dd0e-511e-4d4a-bbbb-3c8d786308dc · outbound
This paper cites Generative adversar- ial nets.Advances in Neural Information Processing Systems, 27, 2014.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Generative adversar- ial nets.Advances in Neural Information Processing Systems, 27, 2014
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:01.045086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2d1c802e-7f3d-4aa1-827f-16ca03c88ed6 · outbound
This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Deep unsupervised learning using nonequilibrium thermodynamics
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:00.917441Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d586b6bb-881c-4746-8140-da0d4b206638 · outbound
This paper cites High-resolution image synthesis with latent diffusion models.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes High-resolution image synthesis with latent diffusion models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:19:50.273756Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6eecfc6b-690f-4ef5-8c76-7572b8537000 · outbound
This paper cites Advance- ments and challenges in deepfake medical imaging: gen- eration and detection techniques.Computers and Elec- trical Engineering, 132:111038, 2026.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Advance- ments and challenges in deepfake medical imaging: gen- eration and detection techniques.Computers and Elec- trical Engineering, 132:111038, 2026
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:00.755758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ab52177d-3079-4b27-ae2b-038bb23d3bd9 · outbound
This paper cites Machine learn- ing based medical image deepfake detection: A com- parative study.Machine Learning with Applications, 8:100298, 2022.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Machine learn- ing based medical image deepfake detection: A com- parative study.Machine Learning with Applications, 8:100298, 2022
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:00.583933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation fea04c3d-ef04-4469-b7df-196934c0becc · outbound
This paper cites {CT-GAN}: Malicious tampering of 3d medi- cal imagery using deep learning.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes {CT-GAN}: Malicious tampering of 3d medi- cal imagery using deep learning
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:00.404674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d32d7a12-2eee-4d15-8706-2efe7f3b27fc · outbound
This paper cites M3dsynth: A dataset of medical 3d images with ai-generated local ma- nipulations.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes M3dsynth: A dataset of medical 3d images with ai-generated local ma- nipulations
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:00.254437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 72f93f72-226d-4097-98b3-892a917fa583 · outbound
This paper cites Hybrid multimodal deepfake detection in medical images using convnextv2- tiny with triplet attention.Journal of Computational Science, page 102815, 2026.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Hybrid multimodal deepfake detection in medical images using convnextv2- tiny with triplet attention.Journal of Computational Science, page 102815, 2026
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:20:00.110342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 46c76f02-65a2-4b92-a793-53f5376f3847 · outbound
This paper cites Forensic detection of generated mri imagery using au- toregressive modeling and frequency analysis.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Forensic detection of generated mri imagery using au- toregressive modeling and frequency analysis
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.931551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0c09a233-9171-4e63-b94b-a1b1c80978fe · outbound
This paper cites Back-in-time diffusion: Unsupervised detection of medical deepfakes.ACM Transactions on Intelligent Systems and Technology, 16(6):1–26, 2025.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Back-in-time diffusion: Unsupervised detection of medical deepfakes.ACM Transactions on Intelligent Systems and Technology, 16(6):1–26, 2025
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.796353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3ddbdec4-12de-4867-8045-107c5adf9a23 · outbound
This paper cites A comprehensive review of deepfakes in medical imaging: ethical concerns, detection tech- niques and future directions.Applied Computer Science, 21(2):139–153, 2025.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes A comprehensive review of deepfakes in medical imaging: ethical concerns, detection tech- niques and future directions.Applied Computer Science, 21(2):139–153, 2025
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.644594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8f483e15-c41e-4250-8f60-725cffbd1378 · outbound
This paper cites Jointdiffusion: Joint representation 9 learning for generative, predictive, and self-explainable ai in healthcare.Computerized Medical Imaging and Graphics, page 102619, 2025.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Jointdiffusion: Joint representation 9 learning for generative, predictive, and self-explainable ai in healthcare.Computerized Medical Imaging and Graphics, page 102619, 2025
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.473614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation adc32c53-88d9-4bad-98e4-07132a0f8586 · outbound
This paper cites Attention-based deep multiple instance learning.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Attention-based deep multiple instance learning
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.330899Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d136b26d-a6ff-4b2b-bac4-c71f0375103a · outbound
This paper cites Grad-cam: Visual explanations from deep net- works via gradient-based localization.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Grad-cam: Visual explanations from deep net- works via gradient-based localization
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.238865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 469e4bd4-b480-4ce9-9071-b8c57d291dc8 · outbound
This paper cites Grad- cam++: Generalized gradient-based visual explanations for deep convolutional networks.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Grad- cam++: Generalized gradient-based visual explanations for deep convolutional networks
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:59.100298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6a1a32f2-18c4-49e2-8b7d-af755d66085d · outbound
This paper cites Faceforensics++: Learning to detect manipulated facial images.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Faceforensics++: Learning to detect manipulated facial images
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:19:51.363795Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9550590e-82d7-429d-a06a-55cfbc747181 · outbound
This paper cites Exploiting visual artifacts to expose deepfakes and face manipulations.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Exploiting visual artifacts to expose deepfakes and face manipulations
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:58.920660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 874354f5-adb6-49a1-b997-68ed0302a9b8 · outbound
This paper cites Mesonet: a compact facial video forgery detection network.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Mesonet: a compact facial video forgery detection network
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:58.746615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5b0866b1-a26e-468d-8b3c-c1cf0becc133 · outbound
This paper cites Cnn-generated images are surprisingly easy to spot.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Cnn-generated images are surprisingly easy to spot
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:58.520754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f8fbebe8-6787-4c43-9aa5-c1ac7f8a5f8c · outbound
This paper cites Lever- aging frequency analysis for deep fake image recogni- tion.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Lever- aging frequency analysis for deep fake image recogni- tion
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:58.333285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2c9cf01b-82f0-4113-afff-b9d34d9dcebb · outbound
This paper cites Rethinking the up- sampling operations in cnn-based generative network for generalizable deepfake detection.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Rethinking the up- sampling operations in cnn-based generative network for generalizable deepfake detection
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:19:52.088330Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e630b279-5b8d-42c4-adb6-5130c0faf381 · outbound
This paper cites Dˆ 3: Scaling up deepfake detection by learning from discrepancy.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Dˆ 3: Scaling up deepfake detection by learning from discrepancy
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:58.128378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 04a2c41e-a01d-4aeb-8b74-1d706e406ef3 · outbound
This paper cites Deepfeaturex-sn: Generalization of deepfake de- tection via contrastive learning.Multimedia Tools and Applications, pages 1–20, 2025.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Deepfeaturex-sn: Generalization of deepfake de- tection via contrastive learning.Multimedia Tools and Applications, pages 1–20, 2025
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:57.923381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8dcd16c2-42b0-47d4-8706-4ac2f1d80bce · outbound
This paper cites On the detection of synthetic images generated by diffu- sion models.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes On the detection of synthetic images generated by diffu- sion models
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:57.783562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 73b9394b-0511-47fb-8d48-89146bfd3e58 · outbound
This paper cites On the exploitation of dct-traces in the generative- ai domain.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes On the exploitation of dct-traces in the generative- ai domain
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:57.627872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d1d6d772-d7cb-402b-b1f3-860cfbd0e592 · outbound
This paper cites Mednet: Medi- cal deepfakes detection using an improved deep learn- ing approach.Multimedia Tools and Applications, 83(16):48357–48375, 2024.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Mednet: Medi- cal deepfakes detection using an improved deep learn- ing approach.Multimedia Tools and Applications, 83(16):48357–48375, 2024
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:57.425779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3d71ece5-99f6-4ee8-9c81-db571b90b4b9 · outbound
This paper cites Image manipulation detection by multi- view multi-scale supervision.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Image manipulation detection by multi- view multi-scale supervision
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:57.221591Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4c2006aa-5d4d-45bb-a801-e223771789cf · outbound
This paper cites Mantra-net: Manipulation tracing network for detec- tion and localization of image forgeries with anomalous features.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Mantra-net: Manipulation tracing network for detec- tion and localization of image forgeries with anomalous features
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:57.011723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4421097f-6abf-41f9-b60a-aca6d699fdd5 · outbound
This paper cites Trufor: Leverag- ing all-round clues for trustworthy image forgery detec- tion and localization.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Trufor: Leverag- ing all-round clues for trustworthy image forgery detec- tion and localization
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:56.844618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ffa251d9-8c05-4157-8433-3661b821ef1e · outbound
This paper cites Dietterich, Richard H.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Dietterich, Richard H
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:56.641798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 624ffc06-d725-4811-a0c3-1444a0e499c4 · outbound
This paper cites Data-efficient and weakly supervised computational pathology on whole-slide images.Nature Biomedical Engineering, 5(6):555–570, 2021.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Data-efficient and weakly supervised computational pathology on whole-slide images.Nature Biomedical Engineering, 5(6):555–570, 2021
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:56.472429Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 43b3c631-5671-4fce-9d80-9f0493e22eff · outbound
This paper cites Dual-stream multi- ple instance learning network for whole slide image clas- sification with self-supervised contrastive learning.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Dual-stream multi- ple instance learning network for whole slide image clas- sification with self-supervised contrastive learning
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:56.316084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation da8736fa-4749-487d-be1f-ce3a6cf64149 · outbound
This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in Neural Informa- tion Processing Systems, 34:2136–2147, 2021.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in Neural Informa- tion Processing Systems, 34:2136–2147, 2021
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:56.131519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4e2e673f-9173-4921-ad57-f307050f208c · outbound
This paper cites Dtfd-mil: Double-tier feature distillation mul- tiple instance learning for histopathology whole slide image classification.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Dtfd-mil: Double-tier feature distillation mul- tiple instance learning for histopathology whole slide image classification
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:55.942758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 71b66178-6599-40f7-86fa-67deec9bbe60 · outbound
This paper cites Learning deep features for discriminative localization.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Learning deep features for discriminative localization
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:55.804264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d3c0820c-8e0f-48c5-87f1-6bb6e7faea8d · outbound
This paper cites Sanity checks for saliency maps.Advances in Neural Informa- tion Processing Systems, 31, 2018.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Sanity checks for saliency maps.Advances in Neural Informa- tion Processing Systems, 31, 2018
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:55.677845Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 306a8b2d-1356-4bb5-9904-81e16cbb694a · outbound
This paper cites Attention is all you need.Advances in Neural Information Processing systems, 30, 2017.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Attention is all you need.Advances in Neural Information Processing systems, 30, 2017
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:55.465087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e2934cf1-6178-4371-a3cf-562a42538839 · outbound
This paper cites an unresolved cited work.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Unresolved cited work
Reference 39
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T05:19:55.282197Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ed5a440f-da24-4fa8-b1c4-b6eb4b7e4ab3 · outbound
This paper cites Deep residual learning for image recognition.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Deep residual learning for image recognition
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:19:53.518309Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f86d9dd-02ec-4353-b468-1e1bf9591a90 · outbound
This paper cites Frequency-aware deep- fake detection: Improving generalizability through fre- quency space domain learning.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Frequency-aware deep- fake detection: Improving generalizability through fre- quency space domain learning
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:55.116281Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 050a6702-a5ff-4523-a9de-dec3cea1d998 · outbound
This paper cites Xception: Deep learning with depth- wise separable convolutions.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Xception: Deep learning with depth- wise separable convolutions
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:54.937409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1b4a2f20-3fc8-4617-a727-bcd368dc144c · outbound
This paper cites Localization of deep in- painting using high-pass fully convolutional network.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Localization of deep in- painting using high-pass fully convolutional network
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:54.785824Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 73a2e251-d644-4b28-b76d-4998f1eb6d5c · outbound
This paper cites A closer look at spatiotemporal convolutions for action recognition.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes A closer look at spatiotemporal convolutions for action recognition
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:54.627841Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bc8d207e-166a-4eb9-b544-3e24181a0257 · outbound
This paper cites Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? InProceedings of the IEEE con- ference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? InProceedings of the IEEE con- ference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:54.504527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 02da23ca-53c5-41e5-97d8-6fb024a34b09 · outbound
This paper cites Vivit: A video vision transformer.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Vivit: A video vision transformer
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:19:53.972051Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b671afac-daad-43b6-816f-27256df4003a · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes An image is worth 16x16 words: Transformers for image recognition at scale
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:19:54.014305Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf89880c-73bb-4705-af66-2d058a87c3ea · outbound
This paper cites Neural machine translation by jointly learning to align and translate.
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes Neural machine translation by jointly learning to align and translate
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:19:54.339209Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.