Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:13:17.682180Z
Paper Citation Record · LEDGER
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography
As of 17 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2507.14102.
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 2026-08-17T06:30:58.91139+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
77 of 77 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7678c27a-d3c8-4130-865b-e7744357f7cc · outbound
This paper cites Rajendra Acharya, Ryszard Tadeusiewicz, and Saeid Nahavandi.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Rajendra Acharya, Ryszard Tadeusiewicz, and Saeid Nahavandi
Reference 1
- Resolution
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Observation 908911c2-363c-434b-a65e-af5cc8283081 · outbound
This paper cites Frangi, U.
Reference 2
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Source-reported events for the cited work
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Observation 699052ef-5845-4978-8529-00b7f83fac4f · outbound
This paper cites Al-Yasriy.
Reference 3
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e79352a8-6f34-4430-a133-ddf6a3bf350a · outbound
This paper cites The iq-othnccd lung cancer dataset,.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography The iq-othnccd lung cancer dataset,
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 1f7b7dfb-0e7b-4616-bab9-a10719236ddf · outbound
This paper cites Generating synthetic computed tomography (ct) im- ages to improve the performance of machine learning model for pediatric abdominal anomaly detection.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Generating synthetic computed tomography (ct) im- ages to improve the performance of machine learning model for pediatric abdominal anomaly detection
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b8a296d2-3d86-4a3a-9aab-bac48ed36197 · outbound
This paper cites Weight uncertainty in neural networks,.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Weight uncertainty in neural networks,
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b321f43e-455c-4b00-a87f-adead6ba5fba · outbound
This paper cites The diagnos- tic evaluation of convolutional neural network (cnn) for the assessment of chest x-ray of patients infected with covid-19.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography The diagnos- tic evaluation of convolutional neural network (cnn) for the assessment of chest x-ray of patients infected with covid-19
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 46f3c0e4-b0ee-49b2-9549-2ce6d027bd37 · outbound
This paper cites Iglovikov, Eugene Khved- chenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Iglovikov, Eugene Khved- chenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a8da67a2-e53d-4e3b-8132-a6efab8f70ae · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f69fc56e-5747-4f9e-8604-c86346b9830d · outbound
This paper cites CrossViT: Cross-Attention Multi-Scale Vision Trans- former for Image Classification.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography CrossViT: Cross-Attention Multi-Scale Vision Trans- former for Image Classification
Reference 10
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation cd02e1d7-3cdb-4a7e-be63-c32cffb65d9a · outbound
This paper cites Recent advances and clin- ical applications of deep learning in medical image analysis.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Recent advances and clin- ical applications of deep learning in medical image analysis
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 22eda032-bd35-4fb8-a020-a4b204e40e59 · outbound
This paper cites Evil: Evidential inference learn- ing for trustworthy semi-supervised medical image segmen- tation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Evil: Evidential inference learn- ing for trustworthy semi-supervised medical image segmen- tation
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c1754c43-611f-4aba-afbf-dbfef8acfee4 · outbound
This paper cites Evidence-based uncertainty-aware semi- supervised medical image segmentation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Evidence-based uncertainty-aware semi- supervised medical image segmentation
Reference 13
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bf8c4f61-2e6a-4175-b7bc-3498d3f92704 · outbound
This paper cites Pl-net: Progressive learning network for medical image segmentation, 2022.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Pl-net: Progressive learning network for medical image segmentation, 2022
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation c270f1a6-5c72-4cf5-94a4-ecbce0489b3c · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation 4a93713c-d78c-4976-8f67-861de5826759 · outbound
This paper cites An efficient model of residual based convo- lutional neural network with bayesian optimization for the classification of malarial cell images.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography An efficient model of residual based convo- lutional neural network with bayesian optimization for the classification of malarial cell images
Reference 16
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation eaedb90f-8840-4093-b3b1-7edc3ff6d089 · outbound
This paper cites Dima et al.
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation ea3d2a6f-458b-4715-82e1-7f1343ffdf71 · outbound
This paper cites Rconet: Deformable mutual information maximiza- tion and high-order uncertainty-aware learning for robust covid-19 detection.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Rconet: Deformable mutual information maximiza- tion and high-order uncertainty-aware learning for robust covid-19 detection
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 94151a9d-40fb-4f01-b79f-a0e1fffda5f6 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography An image is worth 16x16 words: Transformers for image recognition at scale
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation f4c2fb5f-02c5-4374-9aa9-a3c1c71867e0 · outbound
This paper cites Uncertainty quan- tification for deep unrolling-based computational imaging.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Uncertainty quan- tification for deep unrolling-based computational imaging
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f85ec258-1d8d-447b-860f-a8b35c5621ad · outbound
This paper cites Madu, Ismini Lourentzou, and Mehdi Moradi.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Madu, Ismini Lourentzou, and Mehdi Moradi
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 68ec4065-a68c-4741-951d-bb813a22d2d5 · outbound
This paper cites Al-Yasriy, Muayed Al-Huseiny, Furat Mohsen, Enam Khalil, and Zainab Hassan.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Al-Yasriy, Muayed Al-Huseiny, Furat Mohsen, Enam Khalil, and Zainab Hassan
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 30fd5ac3-012f-4aab-b8d8-6e400075f11e · outbound
This paper cites Evidence reconciled neural network for out-of-distribution detection in medical images.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Evidence reconciled neural network for out-of-distribution detection in medical images
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 08041e77-4b58-4da3-a494-06edb34c1769 · outbound
This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning, 2016.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Dropout as a bayesian approximation: Representing model uncertainty in deep learning, 2016
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 64102d24-f5c7-46fd-96ab-b05f84c052f6 · outbound
This paper cites Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R.S.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R.S
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 58274097-fe03-4808-bb77-96126a6bfebd · outbound
This paper cites A survey on attention mechanisms for medical applications: are we moving toward better algo- rithms? IEEE Access, 10:98909–98935, 2022.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography A survey on attention mechanisms for medical applications: are we moving toward better algo- rithms? IEEE Access, 10:98909–98935, 2022
Reference 26
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6af109bf-bc15-4bb6-8227-6fd23707e47c · outbound
This paper cites Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 819e63eb-fda8-4217-92a7-3f547beb8a5c · outbound
This paper cites Uncertainty-aware convo- lutional neural network for covid-19 x-ray images classifi- cation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Uncertainty-aware convo- lutional neural network for covid-19 x-ray images classifi- cation
Reference 28
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b4900538-0337-4114-bc01-efb3372ba8cc · outbound
This paper cites Deep residual learning for image recognition, 2015.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Deep residual learning for image recognition, 2015
Reference 29
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7ce5987e-888a-4f25-a9c7-be9e7ea9a96a · outbound
This paper cites Supervised uncertainty quantifi- cation for segmentation with multiple annotations.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Supervised uncertainty quantifi- cation for segmentation with multiple annotations
Reference 30
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c499cfe4-ee55-4fce-a490-9f112527ca51 · outbound
This paper cites Multi-view eviden- tial learning-based medical image segmentation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Multi-view eviden- tial learning-based medical image segmentation
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c3a0031c-f5dd-42f6-a565-86d018a6b539 · outbound
This paper cites Densely connected convolutional net- works.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Densely connected convolutional net- works
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 31947c09-6a59-4c23-8bee-0aca2fcaf1b8 · outbound
This paper cites Lymphoma segmentation from 3d pet-ct images using a deep evidential network.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Lymphoma segmentation from 3d pet-ct images using a deep evidential network
Reference 33
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e9ad59f2-0bbe-460b-9d8b-d95cb5d475b8 · outbound
This paper cites Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmenta- tion.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmenta- tion
Reference 34
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9ca6c637-0856-4f11-ac5c-7dc9d31a1106 · outbound
This paper cites Qureshi, Jianqiang Li, and Tariq Mahmood.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Qureshi, Jianqiang Li, and Tariq Mahmood
Reference 35
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e81335a7-487d-4845-87d8-dcd961820b81 · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 36
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f1c90780-21c8-4bda-bf76-743b1c045471 · outbound
This paper cites Principles of subjective networks.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Principles of subjective networks
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:24.527331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6e3248cc-3965-4a26-9bfd-4289958d2139 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Adam: A Method for Stochastic Optimization
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation 07ebe9eb-514e-4047-bf80-2b6d40532a8a · outbound
This paper cites An adaptive region- based transformer for nonrigid medical image registration with a self-constructing latent graph.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography An adaptive region- based transformer for nonrigid medical image registration with a self-constructing latent graph
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:24.381981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3c5e3fac-da6d-4d7d-b7e3-9d89ef3113e0 · outbound
This paper cites Drt: Deformable region-based transformer for nonrigid medical image regis- tration with a constraint of orientation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Drt: Deformable region-based transformer for nonrigid medical image regis- tration with a constraint of orientation
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:24.216431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0fce642a-05be-49d8-979a-0a211eba23f8 · outbound
This paper cites Region- based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Region- based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:24.072551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9b40892a-9d08-4683-a26b-b6a1af83a8d6 · outbound
This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9992–10002, 2021.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Swin transformer: Hierarchical vision transformer using shifted windows.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9992–10002, 2021
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e8db8d2a-e535-4016-8aee-d0dd45f6e3e9 · outbound
This paper cites A convnet for the 2020s, 2022.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography A convnet for the 2020s, 2022
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 193705d2-fc9d-409e-b3ac-5b83839dbe4e · outbound
This paper cites Sgdr: Stochastic gradient descent with warm restarts, 2017.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Sgdr: Stochastic gradient descent with warm restarts, 2017
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:23.613605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c200ae51-8df7-4638-ba09-12f98c434718 · outbound
This paper cites Trustworthy multimodal regression with mixture of normal-inverse gamma distribu- tions.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Trustworthy multimodal regression with mixture of normal-inverse gamma distribu- tions
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:23.334737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a793a872-0dc9-425c-9c62-4a7b52e5c75f · outbound
This paper cites Shufflenet v2: Practical guidelines for efficient cnn architec- ture design.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Shufflenet v2: Practical guidelines for efficient cnn architec- ture design
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:22.559674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4a6831a3-0360-465a-95c3-23fc3e616390 · outbound
This paper cites Robinson, Bernhard Kainz, and Daniel Rueckert.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Robinson, Bernhard Kainz, and Daniel Rueckert
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:22.068567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b621296a-e1f4-4246-ad21-c775de2b7b0e · outbound
This paper cites Gastrointestinal abnormality detec- tion and classification using empirical wavelet transform and deep convolutional neural network from endoscopic images.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Gastrointestinal abnormality detec- tion and classification using empirical wavelet transform and deep convolutional neural network from endoscopic images
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:21.892759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e85f4b48-4923-4ddf-9dea-556694ab8fc3 · outbound
This paper cites Kaplano ˘glu, and A.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Kaplano ˘glu, and A
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:21.817037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c1f133e4-5185-4633-9c70-39a0aa269c37 · outbound
This paper cites Abnormality clas- sification and localization using dual-branch whole-region- based cnn model with histopathological images.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Abnormality clas- sification and localization using dual-branch whole-region- based cnn model with histopathological images
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:21.690060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 87b58717-cb47-4078-be11-fe7304632537 · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 51
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T16:13:21.484326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f57008e2-e57e-4b72-b903-a6d5d98a48de · outbound
This paper cites Progres- sive generative adversarial network for generating high- dimensional and wide-frequency signals in intelligent fault diagnosis.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Progres- sive generative adversarial network for generating high- dimensional and wide-frequency signals in intelligent fault diagnosis
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:21.351873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b744f8b2-0355-4f29-ade7-c1186e78c0fc · outbound
This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks, 2019.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Mobilenetv2: Inverted residuals and linear bottlenecks, 2019
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:21.178040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d4cee9b6-a83a-4b66-ab56-61c41059f9a9 · outbound
This paper cites Eviden- tial deep learning to quantify classification uncertainty, 2018.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Eviden- tial deep learning to quantify classification uncertainty, 2018
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:20.964771Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4ac7d4bf-78ea-4eb0-8b72-0a209dc1b6b4 · outbound
This paper cites Kebria, Darius Nahavandi, Saeid Nahavandi, and Dipti Srinivasan.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Kebria, Darius Nahavandi, Saeid Nahavandi, and Dipti Srinivasan
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:20.762789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 025b9fb2-a13e-40ab-8437-ba6af12a0169 · outbound
This paper cites Dual-level deep ev- idential fusion: Integrating multimodal information for en- hanced reliable decision-making in deep learning.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Dual-level deep ev- idential fusion: Integrating multimodal information for en- hanced reliable decision-making in deep learning
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:20.531332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 99b3842a-a938-4f11-9dc2-d3536c3283f5 · outbound
This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:13:15.791093Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30b54ec4-dab2-478a-81ff-da89cc392e6d · outbound
This paper cites Optimizing mcmc-driven bayesian neural networks for high-precision medical image classification in small sample sizes, 2024.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Optimizing mcmc-driven bayesian neural networks for high-precision medical image classification in small sample sizes, 2024
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:20.369609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 152e3849-2948-4649-855f-2b57e499f20b · outbound
This paper cites Deep evidential learning for radiotherapy dose prediction.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Deep evidential learning for radiotherapy dose prediction
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:20.246707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0ca0b8fd-93c6-48c4-a59b-41ccc19ecc74 · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T16:13:20.144037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation db5f5420-db8c-4430-bc94-af57ead1e781 · outbound
This paper cites X-ray and ct-scan- based automated detection and classification of covid-19 us- ing convolutional neural networks (cnn).
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography X-ray and ct-scan- based automated detection and classification of covid-19 us- ing convolutional neural networks (cnn)
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:20.043610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 307b262c-a423-4ce5-9e1b-00b86d5739fc · outbound
This paper cites Training data-efficient image transformers & distillation through at- tention, 2021.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Training data-efficient image transformers & distillation through at- tention, 2021
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.947320Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 84a6465b-0932-4b2e-b03b-d9756cdb2946 · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 63
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T16:13:19.834609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 41a2ed96-01b2-4a88-8fa5-480bee807247 · outbound
This paper cites Alsaadi, and Nianyin Zeng.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Alsaadi, and Nianyin Zeng
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.722670Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c3f145e2-44e4-4360-9eb5-5f4a0bc5d9ac · outbound
This paper cites Co- scale conv-attentional image transformers.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Co- scale conv-attentional image transformers
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.596805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation af0cec42-6503-46cb-8bce-33c3e334cf91 · outbound
This paper cites Isanet: Non-small cell lung cancer classification and detection based on cnn and attention mechanism.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Isanet: Non-small cell lung cancer classification and detection based on cnn and attention mechanism
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.499158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6097b6ac-0331-4b02-b7d0-61213c08fee7 · outbound
This paper cites WSSADN: A Weakly Supervised Spherical Age-Disentanglement Net- work for Detecting Developmental Disorders with Structural MRI.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography WSSADN: A Weakly Supervised Spherical Age-Disentanglement Net- work for Detecting Developmental Disorders with Structural MRI
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.373974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e7cdf2cd-f727-49e6-86ab-597d5c62e6df · outbound
This paper cites Uncertainty quantification in medical im- age segmentation with multi-decoder u-net.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Uncertainty quantification in medical im- age segmentation with multi-decoder u-net
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.250005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 961c6abf-6578-4ef9-86a2-e2b1d5e9deac · outbound
This paper cites Brain ct image classification based on mask rcnn and atten- tion mechanism.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Brain ct image classification based on mask rcnn and atten- tion mechanism
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.142022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9689ba1d-276e-405f-96c2-8454e10052fd · outbound
This paper cites Three- way image classification with evidential deep convolutional neural networks.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Three- way image classification with evidential deep convolutional neural networks
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:19.034402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 63d8269f-c748-4de6-92aa-51523102d376 · outbound
This paper cites Monte-carlo frequency dropout for predic- tive uncertainty estimation in deep learning.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Monte-carlo frequency dropout for predic- tive uncertainty estimation in deep learning
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:18.903993Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3fdeffb6-0770-4cb4-956e-8a427ccb9388 · outbound
This paper cites An evidential-enhanced tri-branch consistency learning method for semi-supervised medical image segmentation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography An evidential-enhanced tri-branch consistency learning method for semi-supervised medical image segmentation
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:18.660549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5a7e5bbe-db33-40e0-8ab8-1ba73eb1369c · outbound
This paper cites Evidence modeling for reliabil- ity learning and interpretable decision-making under multi- modality medical image segmentation.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Evidence modeling for reliabil- ity learning and interpretable decision-making under multi- modality medical image segmentation
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:18.349280Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 894fbf28-74a4-4226-ad70-a0b0485f9728 · outbound
This paper cites COVID-CT-Dataset: A CT Scan Dataset about COVID-19.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:13:17.512947Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8fdfc59c-5555-4e49-b0c0-acf65b8f0a73 · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 75
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T16:13:18.086279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 8247f5e5-0b19-45b0-8bf5-8f8e23542638 · outbound
This paper cites an unresolved cited work.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Unresolved cited work
Reference 2021
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T16:13:29.072944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5134c0ac-dd4f-439c-8579-92f91d842538 · outbound
This paper cites 1 UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Supplementary Material A1.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography 1 UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography Supplementary Material A1
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:13:17.955132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.