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Paper Citation Record · LEDGER

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

As of 15 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:1908.05480.

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

pith.paper-citation-record.v1
1908.05480 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:15:55.210688Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

16 of 16 outbound references displayed

  • verified exact4
  • verified fuzzy3
  • unresolved7
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2a4ece8-555c-4ac5-9568-c26547801df2 · outbound

This paper cites Adversarial Reprogramming of Neural Networks.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Adversarial Reprogramming of Neural Networks

Reference 5

Resolution
malformed identifier
no resolver link, observed 2026-08-14T13:15:55.143483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.143483Z digest=sha256:5ccfe4ff9d6e4b5c44bfe6d0dbe990c62cd3f87e9187bcc64de5051dfa19b152

Observation fc75ac89-7405-4b1e-960b-a82d79505c45 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:15:55.629655Z

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.

source=pdf_text observed=2026-08-14T13:15:55.179802Z digest=sha256:98ae37c8c0ab78ee123dc27d0442421d2460c70de9dc4173b2eb37f64c038b7c

Observation eed425b0-55e9-43a5-b8fc-bc6c2681c440 · outbound

This paper cites Deep Learning with Mixed Supervision for Brain Tumor Segmentation.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Deep Learning with Mixed Supervision for Brain Tumor Segmentation

Reference 12

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unresolved
no resolver link, observed 2026-08-14T13:15:55.185044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.185044Z digest=sha256:3c3cd9ef1ddf1f4be009ba011b4d922c409777043349b8b6a7134dfba479868d

Observation 71bf0994-655b-4edc-8df8-c3dad4c195ac · outbound

This paper cites 3D MRI brain tumor segmentation using autoencoder regularization.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems 3D MRI brain tumor segmentation using autoencoder regularization

Reference 13

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unresolved
no resolver link, observed 2026-08-14T13:15:55.190175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.190175Z digest=sha256:4a9283b0820cda1c2dec0c6f58b1a9715a656615dfb26dad23561366504f4a78

Observation 0135e435-bc15-45b4-a61d-f980d7321c41 · outbound

This paper cites Cyclical learning rates for training neural networks.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Cyclical learning rates for training neural networks

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:15:55.612378Z

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.

source=pdf_text observed=2026-08-14T13:15:55.199389Z digest=sha256:9989ef1f263fb599fa44b1759f574a1a9432766fc29fe0ff1dd5e3b7a5fbb3ac

Observation 983f957c-5bb9-45a5-9472-278e475e875b · outbound

This paper cites A Survey of Unsupervised Deep Domain Adaptation.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems A Survey of Unsupervised Deep Domain Adaptation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-14T13:15:55.205038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.205038Z digest=sha256:8699fefd322253a2df7d8a8761ff25197e1de90c1b69435ff18de733501e6661

Observation c2f5d862-b68d-4379-9d2d-bb26ad56e052 · outbound

This paper cites an unresolved cited work.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:15:55.594922Z

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.

source=pdf_text observed=2026-08-14T13:15:55.210688Z digest=sha256:97138e8bd6fca173371c061c115abacdb4dd8001f79b8a70d38af5b311eb69d2

Observation 8fb8c2b2-f19c-4825-9079-fef1b3e86024 · outbound

This paper cites Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

Reference 1999

Resolution
verified exact
local_arxiv, observed 2026-08-14T13:15:55.442927Z

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.

source=pdf_text observed=2026-08-14T13:15:55.155340Z digest=sha256:3d8f54342a032633e170cfb21ed109cde4f12b43cce96915e52f4704e895a435

Observation 6bc778e5-022e-4eeb-9cc7-c22f6d59b611 · outbound

This paper cites doi: 10.1016/S1361-8415(02)00056-7.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems doi: 10.1016/S1361-8415(02)00056-7

Reference 2002

Resolution
verified exact
doi, observed 2026-08-14T13:15:55.258320Z

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.

source=pdf_text observed=2026-08-14T13:15:55.194937Z digest=sha256:1bca3ad7297cb088db646ab29610846aca704476722f9bf81c7e9b287951d995

Observation 5ce6afb6-2a4c-45be-894f-b4763b3b5d35 · outbound

This paper cites Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound

Reference 2012

Resolution
metadata mismatch
local_arxiv, observed 2026-08-14T13:15:55.521009Z

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.

source=pdf_text observed=2026-08-14T13:15:55.136928Z digest=sha256:33a3b2c69f9a3ca624307f6103784c795d9b3b9ca0311948f55e575a780a5708

Observation c96e4e5e-ca68-42b5-9020-0573731d98f0 · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 2013

Resolution
unresolved
no resolver link, observed 2026-08-14T13:15:55.149124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.149124Z digest=sha256:fcd5b58a09e761a831fdbe9b3088ee87e00b887d10de0185f255e5296df68178

Observation 99eec3f2-a0ec-4bd9-a60f-b7f48c4db82e · outbound

This paper cites Adversarial Networks for the Detection of Aggressive Prostate Cancer.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Adversarial Networks for the Detection of Aggressive Prostate Cancer

Reference 2015

Resolution
verified exact
local_arxiv, observed 2026-08-14T13:15:55.416954Z

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.

source=pdf_text observed=2026-08-14T13:15:55.160873Z digest=sha256:cdff282288335cf5e27cf1928d970071f07199d9bb1decdbbde8e5ed99f32344

Observation 4b527104-25cb-4f4c-8a7b-711e4c211eb0 · outbound

This paper cites Acute and sub-acute stroke lesion segmentation from multimodal MRI.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Acute and sub-acute stroke lesion segmentation from multimodal MRI

Reference 2016

Resolution
verified exact
local_arxiv, observed 2026-08-14T13:15:55.559908Z

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.

source=pdf_text observed=2026-08-14T13:15:55.130636Z digest=sha256:9e804b0f7a5cfb86cb5ee1364a184e7774b33efcb870bf5d048f68774d5f73fa

Observation 8b9edb7e-e3c3-4383-b544-2dbd32830eed · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats).

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems The multimodal brain tumor image segmentation benchmark (brats)

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:15:55.652797Z

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.

source=pdf_text observed=2026-08-14T13:15:55.172748Z digest=sha256:46b06492c649a09a5a7feb950b7f561b370fb6a265e25adbc4508fead1f701f7

Observation 83fbcb30-212d-4292-aceb-351fff167755 · outbound

This paper cites The Deep Weight Prior.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems The Deep Weight Prior

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-14T13:15:55.125591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.125591Z digest=sha256:b41ce3fb2598442cbbbd0d0046f39da6720f5d35e49b6298b417b26945bb7061

Observation f60d888c-ce7b-46ec-9b6b-c608f45d0d28 · outbound

This paper cites doi: 10.3389/fnins.2019.00097.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems doi: 10.3389/fnins.2019.00097

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-14T13:15:55.166976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.166976Z digest=sha256:0577ab526c69995f53f947f0402c4d62d92c5a1520ab8be4f524c0a6ff90231e

Pith citing papers

No inbound Pith citation observations are available.