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Paper Citation Record · LEDGER

Adversarial robustness of a U-Net-based model observer for CT protocol optimization

As of 22 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2606.30115.

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pith.paper-citation-record.v1
2606.30115 v1

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measured 53 of 53 reference resolution

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Reference resolution

53 of 53 outbound references displayed

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Outbound references

Observation 4aa2a3a6-ffa7-439d-9a41-76d48e8b8428 · outbound

This paper cites an unresolved cited work.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Unresolved cited work

Reference 1

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Observation 578cc10c-dcaa-4174-a571-13612b76f999 · outbound

This paper cites Threat of adversarial attacks on deep learning in computer vision: a survey.IEEE Access, 6:14410–14430, 2018.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Threat of adversarial attacks on deep learning in computer vision: a survey.IEEE Access, 6:14410–14430, 2018

Reference 2

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Observation 636b7340-62fc-4a35-ac83-c131c8d5323f · outbound

This paper cites Adversarial attacks and defenses in explainable artificial intelligence: a survey.Information Fusion, 107:102303, 2024.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Adversarial attacks and defenses in explainable artificial intelligence: a survey.Information Fusion, 107:102303, 2024

Reference 3

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Observation 57769a86-8249-4460-a1fa-44285c3628bb · outbound

This paper cites Barrett and Kyle J.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Barrett and Kyle J

Reference 4

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Observation b54422b2-f5da-4039-b7f6-e6a0dfff5f64 · outbound

This paper cites Barrett, Jie Yao, Jannick P.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Barrett, Jie Yao, Jannick P

Reference 5

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Observation 2b7e65be-cff1-4d1c-94ef-973095fd374e · outbound

This paper cites Evasion attacks against machine learning at test time.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Evasion attacks against machine learning at test time

Reference 6

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Observation f0cdebf3-3220-4b33-8f02-db227303c39a · outbound

This paper cites Machine learning robustness: A primer.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Machine learning robustness: A primer

Reference 7

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Observation 90e4c436-0953-404f-ac97-6c1b7876c274 · outbound

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Adversarial robustness of a U-Net-based model observer for CT protocol optimization Unresolved cited work

Reference 8

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Observation 5acd1b3f-e543-4e0f-9247-a12931806692 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Towards evaluating the robustness of neural networks

Reference 9

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Observation 6fb7fc67-1936-4bad-bf62-49195dd8a51e · outbound

This paper cites A survey on adversarial attacks and defences.CAAI Transactions on Intelligence Technology, 6(1):25–45, 2021.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization A survey on adversarial attacks and defences.CAAI Transactions on Intelligence Technology, 6(1):25–45, 2021

Reference 10

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Observation f3519801-fbc4-4cb7-8e6b-9fe8d564af86 · outbound

This paper cites Council directive 2013/59/euratom, 2013.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Council directive 2013/59/euratom, 2013

Reference 11

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Observation 806e7739-3fbc-497d-892b-8ad2f24662a3 · outbound

This paper cites DeGrave, Joseph D.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization DeGrave, Joseph D

Reference 12

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Observation d620aee3-ac2e-4e6c-a6ee-059e78e7ce8a · outbound

This paper cites Survey on adversarial attack and defense for medical image analysis: Methods and challenges.ACM Comput.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Survey on adversarial attack and defense for medical image analysis: Methods and challenges.ACM Comput

Reference 13

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Observation 3d718300-4b41-4e8b-8f39-2bdbd314b735 · outbound

This paper cites Addressing signal alterations induced in CT images by deep learning processing: a preliminary phantom study.Physica Medica, 83:88–100, 2021.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Addressing signal alterations induced in CT images by deep learning processing: a preliminary phantom study.Physica Medica, 83:88–100, 2021

Reference 14

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Observation df49f5c5-f8b2-4064-a0d4-53c07479bf35 · outbound

This paper cites Finlayson, John D.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Finlayson, John D

Reference 15

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Observation 49d60fca-4756-4942-80ae-e336c971e03f · outbound

This paper cites Freiesleben and T.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Freiesleben and T

Reference 16

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Observation f9a0941e-3278-4cda-9647-24ae7f985a10 · outbound

This paper cites The intriguing relation between counterfactual explanations and adversarial examples.Minds and Machines, 32(1):77–109, 2022.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization The intriguing relation between counterfactual explanations and adversarial examples.Minds and Machines, 32(1):77–109, 2022

Reference 17

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Observation 8b4ed217-0ee1-4167-88e1-6a7064dcd664 · outbound

This paper cites Wichmann.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Wichmann

Reference 18

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Observation 65eb6ef4-5d97-4aab-9dc3-4b95520c6125 · outbound

This paper cites Gillies, Paul E.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Gillies, Paul E

Reference 19

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Observation b3fb61b8-ddbe-4df2-a06f-9e4eec146f4b · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Explaining and Harnessing Adversarial Examples

Reference 20

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Observation 57bc72c7-6fd6-4099-87c1-4fbe1f02c596 · outbound

This paper cites Haralick, K.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Haralick, K

Reference 21

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Observation ce182f07-5213-4b2f-96b3-5814b62f6cb5 · outbound

This paper cites Model observers in medical imaging research.Theranostics, 3:774–786, 2013.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Model observers in medical imaging research.Theranostics, 3:774–786, 2013

Reference 22

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Observation a40fb60f-e853-4022-8740-61680a40f91f · outbound

This paper cites Hirano, A.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Hirano, A

Reference 23

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Observation 51796a83-92c8-4c14-9f70-13063d5bc899 · outbound

This paper cites Adversarial examples are not bugs, they are features.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Adversarial examples are not bugs, they are features

Reference 24

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This paper cites Recommendations of the ICRP.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Recommendations of the ICRP

Reference 25

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This paper cites Artificial intelligence (AI) – assessment of the robustness of neural networks – part 1: Overview.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Artificial intelligence (AI) – assessment of the robustness of neural networks – part 1: Overview

Reference 26

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Observation 6ab84a81-4cc3-4ccf-9d9b-a329a55be32e · outbound

This paper cites Adversarial examples in the physical world.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Adversarial examples in the physical world

Reference 27

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Observation 5c119e7f-296f-40a1-b99f-fd6b71710702 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Towards deep learning models resistant to adversarial attacks

Reference 28

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Observation 380ce2e3-7b0d-4681-855a-98a829dfdb2d · outbound

This paper cites Mayerhoefer, Andrzej Materka, Georg Langs, Ida H¨ aggstr¨ om, Piotr Szczypi´ nski, Peter Gibbs, and Gary Cook.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Mayerhoefer, Andrzej Materka, Georg Langs, Ida H¨ aggstr¨ om, Piotr Szczypi´ nski, Peter Gibbs, and Gary Cook

Reference 29

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Observation 8a869b9c-7bbf-4c00-a23d-d316c8e555f9 · outbound

This paper cites Leanpub, 2025.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Leanpub, 2025

Reference 30

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This paper cites Universal adversarial perturbations.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Universal adversarial perturbations

Reference 31

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Observation 034b7806-7a1f-43e9-be8b-194d596326d5 · outbound

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Adversarial robustness of a U-Net-based model observer for CT protocol optimization Stacked hourglass networks for human pose estimation

Reference 32

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Observation 257eac5f-996c-4656-b8d4-f2302383aaff · outbound

This paper cites Adversarial Robustness Toolbox v1.0.0, 2018.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Adversarial Robustness Toolbox v1.0.0, 2018

Reference 33

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Observation 2243a4ef-8ef2-453b-9fbc-a2ac32902552 · outbound

This paper cites Generalizability vs robustness: investigating medical imaging networks using adversarial examples.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Generalizability vs robustness: investigating medical imaging networks using adversarial examples

Reference 34

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Observation e7ceebdb-d418-42a0-b878-5184ede5f548 · outbound

This paper cites Integrating spatial configuration into heatmap regression cnns for landmark localization.Medical Image Analysis, 54:207–219, 2019.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Integrating spatial configuration into heatmap regression cnns for landmark localization.Medical Image Analysis, 54:207–219, 2019

Reference 35

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Observation 910fe02f-e233-4b34-92f2-80fd1e1885d1 · outbound

This paper cites On the relationship between generalization and robustness to adversarial examples.Symmetry, 13(5):817, 2021.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization On the relationship between generalization and robustness to adversarial examples.Symmetry, 13(5):817, 2021

Reference 36

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Observation 8443960d-bd87-4805-bb65-f1088e820566 · outbound

This paper cites Adversarial machine learning: a review of methods, tools, and critical industry sectors.Artificial Intelligence Review, 2025.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Adversarial machine learning: a review of methods, tools, and critical industry sectors.Artificial Intelligence Review, 2025

Reference 37

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Observation a15fdab8-4acb-43bc-86e1-bae61af85def · outbound

This paper cites U-Net: Convolutional networks for biomedical image segmentation.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization U-Net: Convolutional networks for biomedical image segmentation

Reference 38

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:1ed57d62356651b82c15d4545ad4bda73c895a8e13eded6bd18f1ce862f1e16e

Observation 9a234943-ff93-4339-a93a-24fa9efcdf43 · outbound

This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 39

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:bd17ecc27dc6404ad4aab71f6f99ab92c2f50edae9db720c2c61765f9c97b77a

Observation e041246d-8e30-4cd7-98bd-182b6660deca · outbound

This paper cites Adversarial Examples - A Complete Characterisation of the Phenomenon.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Adversarial Examples - A Complete Characterisation of the Phenomenon

Reference 40

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verified exact
local_arxiv, observed 2026-07-01T15:15:47.875925Z

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.

source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:a70f8cafa1f05294a103fc1e90bc0309461aac755898f9cdc5e2cbe75454b92b

Observation d543483f-0cf9-43b7-be6e-32bacc9ec497 · outbound

This paper cites Elkin, and Vijay Devabhaktuni.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Elkin, and Vijay Devabhaktuni

Reference 41

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:a8b95ebbf4a70c6c8c080e18c7e21a165658e5e68dc84c043b8e51faf9047f28

Observation 0d01593a-a51e-4e01-8b24-fdadc46b54fc · outbound

This paper cites Swensson.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Swensson

Reference 42

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:ea910719a7e7da028c757c3716a32585d7c861d5d688935bc5e873916f9531c2

Observation a2816a5d-7233-412f-9801-5b574b01b4b5 · outbound

This paper cites Intriguing properties of neural networks.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Intriguing properties of neural networks

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-07-01T15:15:47.867504Z

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.

source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:6013d14a1a2e5c38ced6b5a6d81a91c3585700440a90a46f2556b4dd30bffd6e

Observation 6e198614-9e30-48b4-8e78-f34cdf8be606 · outbound

This paper cites Thibault, B.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Thibault, B

Reference 44

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:cde9e7f1ccdaeebed0cb50aebd2e9320f8510d00f82e70e749d3a65099a07bbf

Observation 8a4875a5-d65d-48ff-895f-a7b4c6b14c7d · outbound

This paper cites Robustness may be at odds with accuracy.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Robustness may be at odds with accuracy

Reference 45

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:8347a6043615e87b1df1d20e8950ddface12c487a9ca187b56a95ab184551c74

Observation 80898ebd-9cd7-49f0-844b-dead02770ce3 · outbound

This paper cites an unresolved cited work.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Unresolved cited work

Reference 46

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:4a26ebc015e85048d2db7b626d28d144cd5cf88f45876d116519eead0c2d5b43

Observation 5e72e3e0-ae40-4b09-9571-f6cfce3d1214 · outbound

This paper cites Computational radiomics system to decode the radiographic phenotype.Cancer Research, 77(21):e104–e107, 2017.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Computational radiomics system to decode the radiographic phenotype.Cancer Research, 77(21):e104–e107, 2017

Reference 47

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:d2df9e1237dfcba9bec479eb39e87ce8cdf403a7fa615c96eac3ddabe003e57e

Observation 6923c57f-b657-4189-908e-4f43fbf25006 · outbound

This paper cites van Timmeren, Davide Cester, Stephanie Tanadini-Lang, Hatem Alkadhi, and Bettina Baessler.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization van Timmeren, Davide Cester, Stephanie Tanadini-Lang, Hatem Alkadhi, and Bettina Baessler

Reference 48

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no resolver link, observed 2026-06-30T04:09:17.433599Z

Source-reported events for the cited work

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:884291b6be108ed2ec54d73d89cdb4b9b5ffd26ff74f907e4899725cacd38f74

Observation 8ff419a9-4870-4029-b38a-8146ed743873 · outbound

This paper cites Counterfactual explanations without opening the black box: automated decisions and the gdpr.Harvard Journal of Law & Technology, 31(2):841– 887, 2018.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Counterfactual explanations without opening the black box: automated decisions and the gdpr.Harvard Journal of Law & Technology, 31(2):841– 887, 2018

Reference 49

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no resolver link, observed 2026-06-30T04:09:17.433599Z

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:b2d13823319b42852ee311f784b4f1d0043504d554a7b7424021e46f79736ac3

Observation 99b97d5e-9cdd-4e57-85f1-7f1c1c0d58c4 · outbound

This paper cites Welch, Chris McIntosh, Benjamin Haibe-Kains, Michael F.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Welch, Chris McIntosh, Benjamin Haibe-Kains, Michael F

Reference 50

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no resolver link, observed 2026-06-30T04:09:17.433599Z

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:d03ea5338db0eeabda774de348221ca501b336a1215ee85095bf4217cf501a62

Observation fa6ac24d-d499-4569-a158-293e6cd9dd6c · outbound

This paper cites an unresolved cited work.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Unresolved cited work

Reference 51

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:3f8dbd3f6cf0f09edc1de07f286d28700930e8cd63493f264c18a4367794e2b6

Observation dc89934b-a67c-479f-a05d-046321961b7b · outbound

This paper cites Xing, Laurent El Ghaoui, and Michael I.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Xing, Laurent El Ghaoui, and Michael I

Reference 52

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:f8e75a7cc8707d31ad4d3963d5c3423d0e5cb024af9611ddf768032b4773101c

Observation 29ff37be-7d9f-4e49-8656-faa3e94f8381 · outbound

This paper cites Abdalah, Hugo J.

Adversarial robustness of a U-Net-based model observer for CT protocol optimization Abdalah, Hugo J

Reference 53

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source=pdf_text observed=2026-06-30T04:09:17.433599Z digest=sha256:362acdf20f54f6fce23d37218a101a6d964c0fae568861017eb65aafbf357041

Pith citing papers

No inbound Pith citation observations are available.