Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T05:59:01.715677Z
Paper Citation Record · LEDGER
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:1909.00331.
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 2026-08-15T06:32:42.880941+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
45 of 45 outbound references displayed
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- verified fuzzy38
- unresolved7
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fe2ece42-1d26-4aa1-b61f-5990e97b3067 · outbound
This paper cites Gonioscopy findings and prevalence of occludable angles in a burmese population: the meiktila eye study.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Gonioscopy findings and prevalence of occludable angles in a burmese population: the meiktila eye study
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 48915748-5a17-4684-9f91-1fd9f4ad5e09 · outbound
This paper cites Anterior segment imaging for angle closure.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Anterior segment imaging for angle closure
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.176949Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c9506179-9508-4f66-8197-b45c5951d8a3 · outbound
This paper cites The prevalence of primary angle closure glaucoma in adult asians: a systematic review and meta-analysis.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images The prevalence of primary angle closure glaucoma in adult asians: a systematic review and meta-analysis
Reference 3
- Resolution
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- raw_fallback, observed 2026-08-14T05:59:02.167588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 58449231-5103-4116-a51c-dbb47d230e48 · outbound
This paper cites Cumba, Sunita Radhakrishnan, Nicholas P.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Cumba, Sunita Radhakrishnan, Nicholas P
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.158469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ed7d1db0-a6c5-4f8b-8f32-71a214b5972c · outbound
This paper cites A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.146932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation bc4c826f-83bd-4d46-b434-bb2b2c0b44a7 · outbound
This paper cites Drunet: a dilated-residual u-net deep learning network to segment optic nerve head tissues in optical coherence tomography images.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Drunet: a dilated-residual u-net deep learning network to segment optic nerve head tissues in optical coherence tomography images
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.136808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 011cfffd-a692-48b9-9ea0-9d0ea583409b · outbound
This paper cites The impor- tance of skip connections in biomedical image segmentation , pages 179–187.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images The impor- tance of skip connections in biomedical image segmentation , pages 179–187
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.126962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 98cd73de-eb72-4257-9646-c3d4442f2706 · outbound
This paper cites Effect of adjunctive viscogonioplasty on drainage angle status in cataract surgery: a randomized clinical trial.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Effect of adjunctive viscogonioplasty on drainage angle status in cataract surgery: a randomized clinical trial
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 486cf993-bd34-4f93-bfdd-b4b166242169 · outbound
This paper cites Epidemiology of angle-closure glaucoma.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Epidemiology of angle-closure glaucoma
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.105215Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 72d293bb-9b84-445d-b99f-5a1c1cb2a309 · outbound
This paper cites Neural networks and the bias/variance dilemma.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Neural networks and the bias/variance dilemma
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.095885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2647be96-1b2a-49bd-a157-d045c1fc4981 · outbound
This paper cites Framing u-net via deep convolutional framelets: Application to sparse- view ct.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Framing u-net via deep convolutional framelets: Application to sparse- view ct
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.085959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f5d25b44-c272-4251-8675-3b38416d8ad4 · outbound
This paper cites Neural network ensembles.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Neural network ensembles
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.076455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d671a376-c921-4378-9b6c-2a00644d17b0 · outbound
This paper cites Brain tumor segmentation with deep neural networks.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Brain tumor segmentation with deep neural networks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.066450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 637bcae6-6b6a-4a69-b95e-6604b48d630d · outbound
This paper cites Deep residual learning for image recognition.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Deep residual learning for image recognition
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.055816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c3790cca-e415-4ef2-9810-e641c4106003 · outbound
This paper cites Predicting the outcome of laser peripheral iridotomy for primary angle closure suspect eyes using anterior segment optical coherence tomography.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Predicting the outcome of laser peripheral iridotomy for primary angle closure suspect eyes using anterior segment optical coherence tomography
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.046064Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0e4b5d4a-7ce3-4e6a-a479-8dd56d84036f · outbound
This paper cites H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9d39c0d-1c2c-489b-bf36-bfda77a1db47 · outbound
This paper cites Class imbalance problem , pages 171–171.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Class imbalance problem , pages 171–171
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.034963Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d06f6666-f861-4056-854d-55e7d19f47ba · outbound
This paper cites Semantic segmentation of aerial images with an ensemble of cnns.ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 3:473, 2016.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Semantic segmentation of aerial images with an ensemble of cnns.ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 3:473, 2016
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 2026-08-15T06:32:42.880941+00:00.
Observation c959caa7-a244-4542-84ad-5233e2facb70 · outbound
This paper cites Anterior segment imaging in glaucoma: an updated review.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Anterior segment imaging in glaucoma: an updated review
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.012298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2d6b08df-59b3-404d-af03-bd1c78be530b · outbound
This paper cites Qualitative evalua- tion of anterior segment in angle closure disease using anterior segment optical coherence tomography.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Qualitative evalua- tion of anterior segment in angle closure disease using anterior segment optical coherence tomography
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:02.001335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation bd4c7b09-3728-4e58-a645-4d79e7606dda · outbound
This paper cites Nolan, Jovina L.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Nolan, Jovina L
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.990844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2f959385-41ad-4094-9a6c-0d2f1ba4c9b9 · outbound
This paper cites Lens vault, thickness, and position in chinese subjects with angle closure.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Lens vault, thickness, and position in chinese subjects with angle closure
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.980780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9e47fab9-0689-4d72-929f-b678b27d72ca · outbound
This paper cites Novel association of smaller anterior chamber width with angle closure in singaporeans.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Novel association of smaller anterior chamber width with angle closure in singaporeans
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.971165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation addddaad-efc0-4e58-a94c-249b4b485ad3 · outbound
This paper cites Okamoto, K.
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 2026-08-15T06:32:42.880941+00:00.
Observation 718fbcfa-813d-4e04-879f-6bde934076ca · outbound
This paper cites Natural history of glaucoma.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Natural history of glaucoma
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.952196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation decd7d52-a23f-436c-9e1e-c9406977798c · outbound
This paper cites Fullresolution residual net- works for semantic segmentation in street scenes.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Fullresolution residual net- works for semantic segmentation in street scenes
Reference 26
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- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.942349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1c61dbd8-b07d-4f07-b722-fefcee3db781 · outbound
This paper cites Role of anterior segment optical coherence tomog- raphy in angleclosure disease: a review.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Role of anterior segment optical coherence tomog- raphy in angleclosure disease: a review
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.932267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2184a0f5-9030-4970-8dbb-44a62ba7b557 · outbound
This paper cites Angle imaging: advances and challenges.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Angle imaging: advances and challenges
Reference 28
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- raw_fallback, observed 2026-08-14T05:59:01.922425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 84f34709-a3d8-4c82-bb03-ff85ef491244 · outbound
This paper cites an unresolved cited work.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Unresolved cited work
Reference 29
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e85fe95f-6a6e-4f0a-8187-6db984cc8a92 · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images U-net: Convolutional networks for biomedical image segmentation
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.901708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e4c52bd4-c4aa-47ee-b5ac-0f7bede2d0a9 · outbound
This paper cites Sakata, Raghavan Lavanya, David S.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Sakata, Raghavan Lavanya, David S
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.891451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d0ae2060-f98d-49d6-8e3a-1b420871597b · outbound
This paper cites Assessment of the scleral spur in anterior segment optical coherence tomography images.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Assessment of the scleral spur in anterior segment optical coherence tomography images
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.880697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b0a1d524-bda5-4430-858e-ea874d37243e · outbound
This paper cites an unresolved cited work.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Unresolved cited work
Reference 33
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 80f55454-6abc-42c9-98f6-4b9977dc00f7 · outbound
This paper cites Management of angle closure glaucoma.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Management of angle closure glaucoma
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.861080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4f774af4-a552-4d25-93fb-927356e18d59 · outbound
This paper cites OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Reference 35
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af861720-94e1-41d2-91d3-9ac800033169 · outbound
This paper cites Understanding machine learning: From theory to algorithms.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Understanding machine learning: From theory to algorithms
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:59:01.684760Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3a42181d-136a-4c9e-97dc-f05747846248 · outbound
This paper cites Associations of iris structural mea- surements in a chinese population: the singapore chinese eye study.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Associations of iris structural mea- surements in a chinese population: the singapore chinese eye study
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.845927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3b4b3363-27f5-4269-9ef5-6d84b04af522 · outbound
This paper cites Deep neural networks for object detection.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Deep neural networks for object detection
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.836565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d18c9deb-e760-4aef-aa07-387e99fc728c · outbound
This paper cites an unresolved cited work.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Unresolved cited work
Reference 39
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T05:59:01.826917Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d9ab4c83-170d-456b-8842-7e84550f1764 · outbound
This paper cites Wong, Harry A.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Wong, Harry A
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.816730Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9f0f29fe-3e4c-474b-89a3-08af5051c1d9 · outbound
This paper cites Ethnic difference of the anterior chamber area and volume and its association with angle width.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Ethnic difference of the anterior chamber area and volume and its association with angle width
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.807069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b0c009fd-bd24-4aab-8eaa-32feb943f61f · outbound
This paper cites Association of narrow angles with anterior chamber area and volume measured with anterior-segment optical coherence tomography.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Association of narrow angles with anterior chamber area and volume measured with anterior-segment optical coherence tomography
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.796320Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 84c08e98-b279-4c98-9e56-ce7ffafa3a3b · outbound
This paper cites Empirical Evaluation of Rectified Activations in Convolutional Network.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Empirical Evaluation of Rectified Activations in Convolutional Network
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:59:01.708085Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92630ce6-f01a-42df-a9ab-869788ce0402 · outbound
This paper cites Detecting anatomical landmarks from limited medi- cal imaging data using two-stage task-oriented deep neural networks.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Detecting anatomical landmarks from limited medi- cal imaging data using two-stage task-oriented deep neural networks
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.786049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 39c7fec3-cd5b-401f-a28b-ec0b71a4743f · outbound
This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images 3d u-net: learning dense volumetric segmentation from sparse annotation
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:59:01.775086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.