Paper Citation Record · LEDGER

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.06054.

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

pith.paper-citation-record.v1
2506.06054 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:48.307575Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy27
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b58b8a3-a8a4-4230-bbe6-ebf41b586032 · outbound

This paper cites Fetal mri: A force for prenatal imaging of birth defects,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Fetal mri: A force for prenatal imaging of birth defects,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.544826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aac3e6a2-32e5-436b-95fd-69609fdbccc4 · outbound

This paper cites Prenatal diagnosis of congenital heart defects: echocardio- graphy,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Prenatal diagnosis of congenital heart defects: echocardio- graphy,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.538267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df7d9aa4-f0be-470a-bebf-841f84a61ea4 · outbound

This paper cites Role of four-chamber heart ultrasound images in automatic assessment of fetal heart: A systematic understanding,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Role of four-chamber heart ultrasound images in automatic assessment of fetal heart: A systematic understanding,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.529619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14596166-d803-40b5-9ad7-68e2defd8d60 · outbound

This paper cites An in-depth interpretation of the guidelines for prenatal ul- trasound examination (2012) by the sonographers’ association of the chinese medical doctors’ association.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound An in-depth interpretation of the guidelines for prenatal ul- trasound examination (2012) by the sonographers’ association of the chinese medical doctors’ association

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.522510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3fa26c30-8ffc-44e5-9c0b-180cafac944d · outbound

This paper cites Aium practice guideline for the performance of obstetric ultrasound examinations,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Aium practice guideline for the performance of obstetric ultrasound examinations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.515487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 93fea52b-11f6-44db-bf7b-ebad8e556813 · outbound

This paper cites Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.508506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3eb35285-9d62-44a4-8d75-9975fc42993f · outbound

This paper cites Deep learning in image classification using residual network (resnet) variants for detection of colorectal cancer,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Deep learning in image classification using residual network (resnet) variants for detection of colorectal cancer,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.501687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fc162712-994b-4e84-942e-dc54d3a2c76d · outbound

This paper cites Automatic classification of fetal heart rate based on convolutional neural network,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Automatic classification of fetal heart rate based on convolutional neural network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.494745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 32560310-8a7b-4d87-a965-d588ca9888bb · outbound

This paper cites Fetal cardiac cycle detection in multi-resource echocardiograms using hybrid classification framework,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Fetal cardiac cycle detection in multi-resource echocardiograms using hybrid classification framework,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.487796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73cc5557-ce0a-4d68-b73b-34c07630ca35 · outbound

This paper cites Deep endpoints focusing network under geometric constraints for end-to-end biometric measurement in fetal ultrasound images,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Deep endpoints focusing network under geometric constraints for end-to-end biometric measurement in fetal ultrasound images,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.479970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5417101e-4a22-40e8-80cf-4b9611d94ded · outbound

This paper cites Mobileunet-fpn: A semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Mobileunet-fpn: A semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.471746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81bf0ea0-0ab1-4bcd-8570-d824e110e20d · outbound

This paper cites A yolox-based deep instance seg- mentation neural network for cardiac anatomical structures in fetal ultrasound images,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound A yolox-based deep instance seg- mentation neural network for cardiac anatomical structures in fetal ultrasound images,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.463930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d5678a9-b585-4324-82fc-149ce9d1efae · outbound

This paper cites Fetal cardiac ultrasound standard section detection model based on multitask learning and mixed attention mechanism,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Fetal cardiac ultrasound standard section detection model based on multitask learning and mixed attention mechanism,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.455842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e600780c-9f57-4442-915a-e8b5d5951818 · outbound

This paper cites Automatic fetal ultrasound standard plane recognition based on deep learning and iiot,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Automatic fetal ultrasound standard plane recognition based on deep learning and iiot,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.448327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b26c396c-a589-4407-a7e4-b3c84db21deb · outbound

This paper cites An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.441361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb5730b5-d365-4321-8f4d-0d0de22e26f6 · outbound

This paper cites Hfsccd: a hybrid neural network for fetal standard cardiac cycle detection in ultrasound videos,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Hfsccd: a hybrid neural network for fetal standard cardiac cycle detection in ultrasound videos,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.432876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9eb3394-af03-480b-ba3e-7e4702ebabd2 · outbound

This paper cites Unsupervised domain adaptation for anatomical structure detection in ultrasound images,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Unsupervised domain adaptation for anatomical structure detection in ultrasound images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.425790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6af9b18-64f2-4f36-a1d2-2370d9356407 · outbound

This paper cites Sleep staging by bidirectional long short-term memory convolution neural network,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Sleep staging by bidirectional long short-term memory convolution neural network,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.418662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6a15444-9807-4499-8fe5-c47d197de40f · outbound

This paper cites An adaptive meta-imitation learning-based recommendation environment simulator: A case study on ship-cargo matching,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound An adaptive meta-imitation learning-based recommendation environment simulator: A case study on ship-cargo matching,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.411376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0ec81c0-6e89-4b68-a704-fb125830aa1a · outbound

This paper cites M3-uda: A new benchmark for unsupervised domain adaptive fetal cardiac structure detection,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound M3-uda: A new benchmark for unsupervised domain adaptive fetal cardiac structure detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.403556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b1fc099-d1f8-47a5-ab3c-167426b4149c · outbound

This paper cites Farn: fetal anatomy reasoning network for detection with global context semantic and local topology relationship,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Farn: fetal anatomy reasoning network for detection with global context semantic and local topology relationship,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.396348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6deea8a-a6cc-4503-90fc-6594b154e613 · outbound

This paper cites Efficient deep reinforcement learning-enabled recommendation,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Efficient deep reinforcement learning-enabled recommendation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.387829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3cbd40a-492b-4ec6-b25c-3a7fe609e225 · outbound

This paper cites Transfsm: Fetal anatomy segmen- tation and biometric measurement in ultrasound images using a hybrid transformer,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Transfsm: Fetal anatomy segmen- tation and biometric measurement in ultrasound images using a hybrid transformer,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.380074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d422bee-1cab-43f5-93f0-758b98d62a4c · outbound

This paper cites Fetal cardiac structure detection using multi-task learning,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Fetal cardiac structure detection using multi-task learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.371956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 43c3c447-9677-4db3-a064-2bd453aaadc2 · outbound

This paper cites $ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound $ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:04:48.339919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6144a8bf-1edf-47d4-ad91-ec6a933f846f · outbound

This paper cites Understanding adamw through proximal methods and scale-freeness,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Understanding adamw through proximal methods and scale-freeness,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.364509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c7b573b2-7146-4fb9-8567-128a5016e782 · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.356121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9b24f8b-9de1-4de0-a714-570c001d2d3e · outbound

This paper cites Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:48.304865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d6a36a8-bb4f-4089-8627-774c3ffce0df · outbound

This paper cites Conference on computer vision and pattern recognition,.

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound Conference on computer vision and pattern recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:48.348290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.