Paper Citation Record · LEDGER

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2509.03975.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.03975 v2

Coverage vector

measured 56 of 56 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:33:50.835577Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T10:33:56.639846Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation 47a0cc51-58c8-499e-94bc-403cf8491e22 · outbound

This paper cites Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:33:56.710663Z

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Observation b9712390-d34c-4c33-885d-5fcea87306f4 · outbound

This paper cites an unresolved cited work.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Unresolved cited work

Reference 2

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Observation 8252dd14-cc91-4dd7-a5c6-13434f5e9a4e · outbound

This paper cites an unresolved cited work.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Unresolved cited work

Reference 3

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Observation c163a277-8778-4e77-884b-2b2bc28e05c8 · outbound

This paper cites An auxiliary modality available only during training improves the segmentation accuracy of a Y- Net model when applied to new data, even if it is not avail- able during test time.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training An auxiliary modality available only during training improves the segmentation accuracy of a Y- Net model when applied to new data, even if it is not avail- able during test time

Reference 4

Resolution: verified exact; doi, observed 2026-08-05T10:34:05.721493Z

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Observation fed7f729-920f-4c70-8acb-f499dab11392 · outbound

This paper cites If less than8annotations are 6 Table 5.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training If less than8annotations are 6 Table 5

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:05.894144Z

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Observation 5c92ced5-7920-438b-b636-7e8ae478e571 · outbound

This paper cites Does the functional liver imaging score derived from gadoxetic acid–enhanced MRI predict outcomes in chronic liver disease?,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Does the functional liver imaging score derived from gadoxetic acid–enhanced MRI predict outcomes in chronic liver disease?,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:04.610691Z

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Observation 779f21eb-e18f-47ba-8c71-d25987b27b3e · outbound

This paper cites Revisiting the risks of MRI with gadolinium based contrast agents—review of literature and guidelines,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Revisiting the risks of MRI with gadolinium based contrast agents—review of literature and guidelines,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:05.536380Z

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Observation 370c5b81-b115-42fd-a77c-3ba7ff42101a · outbound

This paper cites Ash and nash,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Ash and nash,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:05.362785Z

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Observation 34d0c02c-e57a-40c3-b99f-97d6c3fc9148 · outbound

This paper cites Synergy between nafld and afld and potential biomarkers,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Synergy between nafld and afld and potential biomarkers,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:05.190400Z

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Observation 62d08f3b-1dd8-465f-b255-2dc90d62813b · outbound

This paper cites Hepatic vessels segmentation using deep learning and preprocessing enhancement,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Hepatic vessels segmentation using deep learning and preprocessing enhancement,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:04.998814Z

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Observation ea095d13-6ebf-4e21-a577-ea6dde1624c1 · outbound

This paper cites An automated liver tumour segmenta- tion from abdominal ct scans for hepatic surgical plan- ning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training An automated liver tumour segmenta- tion from abdominal ct scans for hepatic surgical plan- ning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:04.834273Z

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Observation 5def8d55-e577-4499-b294-fb3712ebe984 · outbound

This paper cites Hepatic vessel segmentation using vari- ational level set combined with non-local robust statis- tics,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Hepatic vessel segmentation using vari- ational level set combined with non-local robust statis- tics,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:03.369794Z

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Observation 94fa80e5-d518-4a9b-98ca-49dd0b6f00a7 · outbound

This paper cites Compu- tational methods for liver vessel segmentation in med- ical imaging: A review,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Compu- tational methods for liver vessel segmentation in med- ical imaging: A review,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:04.365478Z

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Observation 8010b59f-1897-44b6-be39-aaaa742ed29a · outbound

This paper cites Multiscale vessel enhance- ment filtering,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Multiscale vessel enhance- ment filtering,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:04.184591Z

Source-reported events for the cited work

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Observation aab67e07-6e11-4e55-95f3-ee21b77f4fbb · outbound

This paper cites Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:03.969971Z

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Observation fff800c6-18bc-418a-974a-df0df21f5f5c · outbound

This paper cites Design and validation of a tool for neurite tracing and analysis in fluorescence mi- croscopy images,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Design and validation of a tool for neurite tracing and analysis in fluorescence mi- croscopy images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:03.751587Z

Source-reported events for the cited work

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Observation 9ed56068-c0d6-4ce8-82b3-e290b75e98d3 · outbound

This paper cites Retinal ves- sel segmentation using the 2-d gabor wavelet and su- pervised classification,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Retinal ves- sel segmentation using the 2-d gabor wavelet and su- pervised classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:03.569454Z

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Observation bc46b22f-403b-47c6-b6b1-b38e7906760d · outbound

This paper cites Robust liver vessel extraction us- ing 3d u-net with variant dice loss function,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Robust liver vessel extraction us- ing 3d u-net with variant dice loss function,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:02.243076Z

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Observation 6fe5f6b2-223a-4ec0-ab36-79806dfb16df · outbound

This paper cites Au- tomatic liver vessel segmentation using 3d region grow- ing and hybrid active contour model,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Au- tomatic liver vessel segmentation using 3d region grow- ing and hybrid active contour model,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:03.162999Z

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Observation c93e75d7-7e77-4044-9ab3-ad27a1d8477e · outbound

This paper cites Accurate liver vessel segmentation via active contour model with dense vessel candidates,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Accurate liver vessel segmentation via active contour model with dense vessel candidates,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:02.940477Z

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Observation d56d3ec2-976d-4ce0-bee5-705369778044 · outbound

This paper cites Automatic segmentation methods for liver and hepatic vessels from ct and mri volumes, applied to the couinaud scheme,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Automatic segmentation methods for liver and hepatic vessels from ct and mri volumes, applied to the couinaud scheme,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:02.729144Z

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Observation fe20b54b-5b68-498d-b9b8-421d10d13423 · outbound

This paper cites A novel method to model hepatic vascular network using vessel segmenta- tion, thinning, and completion,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training A novel method to model hepatic vascular network using vessel segmenta- tion, thinning, and completion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:02.573097Z

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Observation 79a464aa-de9b-499b-ac52-534952e5178c · outbound

This paper cites Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:02.402752Z

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Observation 5d3d0026-e69d-40b9-9465-ec0d92dee983 · outbound

This paper cites Training liver vessel segmentation deep neural net- works on noisy labels from contrast ct imaging,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Training liver vessel segmentation deep neural net- works on noisy labels from contrast ct imaging,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:01.104605Z

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Observation 30a2bef3-3113-432d-8d7a-4b9326e00a57 · outbound

This paper cites Segmen- tation of vascular regions in ultrasound images: A deep learning approach,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Segmen- tation of vascular regions in ultrasound images: A deep learning approach,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:02.032927Z

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Observation 6e475748-2eeb-4749-a311-f8b52c9e9ff8 · outbound

This paper cites Vesselnet: A deep convo- lutional neural network with multi pathways for robust hepatic vessel segmentation,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Vesselnet: A deep convo- lutional neural network with multi pathways for robust hepatic vessel segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:01.841490Z

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Observation ae31d05f-93b6-43d8-ae9c-9eaf87cbc9d2 · outbound

This paper cites an unresolved cited work.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Unresolved cited work

Reference 27

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Observation dfcf2713-79dd-46b3-b682-296a92f1c6fc · outbound

This paper cites Topnet: Topol- ogy preserving metric learning for vessel tree recon- struction and labelling,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Topnet: Topol- ogy preserving metric learning for vessel tree recon- struction and labelling,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:01.688098Z

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Observation 239132fe-f726-44a0-9ff2-4a822b86d18d · outbound

This paper cites Mr-to-us reg- istration using multiclass segmentation of hepatic vas- culature with a reduced 3d u-net,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Mr-to-us reg- istration using multiclass segmentation of hepatic vas- culature with a reduced 3d u-net,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:01.518983Z

Source-reported events for the cited work

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Observation 6465ed4c-58a8-4453-afee-b9b54aec8695 · outbound

This paper cites An attention-guided deep neu- ral network with multi-scale feature fusion for liver ves- sel segmentation,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training An attention-guided deep neu- ral network with multi-scale feature fusion for liver ves- sel segmentation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:01.326072Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 66aea506-8696-4883-9fa9-b61845ea6542 · outbound

This paper cites Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:00.871096Z

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Observation 1aaf3ab1-b5d7-4586-98f7-fbce5b7832fa · outbound

This paper cites Vessel segmentation from abdominal magnetic reso- nance images: adaptive and reconstructive approach,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Vessel segmentation from abdominal magnetic reso- nance images: adaptive and reconstructive approach,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:00.695515Z

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Observation 420989b4-fc4e-4ee6-928a-509c90e61221 · outbound

This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training 3d u-net: learning dense volumetric segmentation from sparse annotation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:00.491091Z

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Observation 3b2df992-7504-4163-9d15-3fbdaa420e2c · outbound

This paper cites Multitask learning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Multitask learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:00.304270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b42aa8f1-42f2-43e6-af41-6196c52dafef · outbound

This paper cites A survey on multi-task learning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training A survey on multi-task learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:34:00.082381Z

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Observation 5828f00c-c2f9-4bf4-a4a0-756b4dbf6dca · outbound

This paper cites Which Tasks Should Be Learned Together in Multi-task Learning?.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Which Tasks Should Be Learned Together in Multi-task Learning?

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:33:56.562183Z

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Observation 55891b9b-7db7-4b93-a633-715c7b4fd667 · outbound

This paper cites Multi- task learning for brain tumor segmentation,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Multi- task learning for brain tumor segmentation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:59.933736Z

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Observation 51c08f53-29ab-4c2c-b4e6-e7f5be26f7ac · outbound

This paper cites Multi-task deep learning based CT imag- ing analysis for covid-19 pneumonia: Classification and segmentation,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Multi-task deep learning based CT imag- ing analysis for covid-19 pneumonia: Classification and segmentation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:59.788464Z

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Observation d499ff4f-34ea-4a1e-ae26-de75af21cb6d · outbound

This paper cites A Survey on Multi-Task Learning.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training A Survey on Multi-Task Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T10:33:54.495513Z

Source-reported events for the cited work

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Observation 6ea5eaa0-b1d3-41bb-8a3d-a203f2d21a8e · outbound

This paper cites Cross-stitch networks for multi-task learning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Cross-stitch networks for multi-task learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:59.602364Z

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Observation bdd55c9d-9100-488b-a672-8f6769e29a6e · outbound

This paper cites Multi-Task Learning for Dense Prediction Tasks: A Survey.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Multi-Task Learning for Dense Prediction Tasks: A Survey

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:33:56.294059Z

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Observation ac81542e-739c-4d16-a393-c678d6839654 · outbound

This paper cites Multi- task learning using uncertainty to weigh losses for scene geometry and semantics,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Multi- task learning using uncertainty to weigh losses for scene geometry and semantics,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:59.419508Z

Source-reported events for the cited work

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Observation dc12704e-29a4-4d59-91e1-5f07c986f162 · outbound

This paper cites Gradnorm: Gradient normaliza- tion for adaptive loss balancing in deep multitask net- works,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Gradnorm: Gradient normaliza- tion for adaptive loss balancing in deep multitask net- works,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:59.208413Z

Source-reported events for the cited work

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Observation b52c5b00-dbe5-4be7-ad8c-b605316fd9b8 · outbound

This paper cites Dynamic task prioritization for multitask learning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Dynamic task prioritization for multitask learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:58.992715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ca90183-cfbc-40a1-8bb1-2f93d9e13286 · outbound

This paper cites End-to-end multi-task learning with attention,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training End-to-end multi-task learning with attention,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:58.809189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 398db62a-bda4-4571-a659-007c95b92a99 · outbound

This paper cites Joint left atrial segmentation and scar quantification based on a dnn with spatial encoding and shape at- tention,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Joint left atrial segmentation and scar quantification based on a dnn with spatial encoding and shape at- tention,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:58.637440Z

Source-reported events for the cited work

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Observation d78d64bd-c836-4c15-961d-190a30a81aac · outbound

This paper cites Y-net: a one-to-two deep learning framework for digital holographic reconstruction,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Y-net: a one-to-two deep learning framework for digital holographic reconstruction,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:58.382786Z

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Observation 34d4b246-7c35-4844-820c-00c01e625dee · outbound

This paper cites Nddr-cnn: Layerwise feature fusing in multi- task cnns by neural discriminative dimensionality re- duction,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Nddr-cnn: Layerwise feature fusing in multi- task cnns by neural discriminative dimensionality re- duction,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:58.157292Z

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Observation d6be0ffc-85c0-472a-bb39-7d867c365004 · outbound

This paper cites Group normalization,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Group normalization,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:57.980312Z

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Observation a8f16a1d-cd2d-40eb-9cd8-7e45ec4d2f70 · outbound

This paper cites Building skeleton models via 3-d medial sur- face axis thinning algorithms,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Building skeleton models via 3-d medial sur- face axis thinning algorithms,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:57.764274Z

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Observation ca13cc60-ac26-4ffb-82d7-5cd01881f7ad · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Adam: A Method for Stochastic Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T10:33:55.538344Z

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Observation cbc794ec-647c-40ca-9deb-df33dd3091c1 · outbound

This paper cites A threshold selection method from gray-level histograms,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training A threshold selection method from gray-level histograms,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:57.597826Z

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Observation 3ec91886-8b80-4f4b-abb3-0845389e5b38 · outbound

This paper cites A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T10:33:55.727933Z

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Observation 36ab1eea-99eb-4d9f-a99c-c9bf3a14f2a6 · outbound

This paper cites Liver segment approximation in ct data for surgical resection planning,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Liver segment approximation in ct data for surgical resection planning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:57.377949Z

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Observation 1dcceff4-1994-4e4a-a940-c0674da7c864 · outbound

This paper cites Vascular branching ge- ometry relating to portal hypertension: a study of liver microvasculature in cirrhotic rats by x-ray phase- contrast computed tomography,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Vascular branching ge- ometry relating to portal hypertension: a study of liver microvasculature in cirrhotic rats by x-ray phase- contrast computed tomography,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:57.150096Z

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Observation cab83541-81b7-4e29-89bf-25544c82b061 · outbound

This paper cites Accurate and ver- satile 3d segmentation of plant tissues at cellular resolu- tion,.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training Accurate and ver- satile 3d segmentation of plant tissues at cellular resolu- tion,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:33:56.902961Z

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Pith citing papers

Observation 47a0cc51-58c8-499e-94bc-403cf8491e22 · inbound