Evidence map›Paper›PMID 39734588›Full record

ArticleJournal of statistical theory and practice2024

Applications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation.

Kaiming Cheng, Yueyang Shen, Ivo D Dinov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of statistical theory and practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kaiming ChengStatistics Online Computational Resource, University of Michigan, 426 North Ingalls Str, Ann Arbor, Michigan 48109-2003.
Yueyang ShenStatistics Online Computational Resource, University of Michigan, 426 North Ingalls Str, Ann Arbor, Michigan 48109-2003.
Ivo D DinovStatistics Online Computational Resource, University of Michigan, 426 North Ingalls Str, Ann Arbor, Michigan 48109-2003.

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disordersR01MH121079 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HYDE, LUKE WILLIAMSON, MITCHELL, COLTER M.S. · 2019 to 2024
$7.2M
Digital behavioral phenotyping and multi-region electrophysiology to determine behavioral and neural network changes underlying the stress response in miceR01MH126137 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WATSON, BRENDON O · 2021 to 2025
$3.3M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
NCATS NIH HHS UL1 TR002240NCI NIH HHS R01 CA233487NIGMS NIH HHS T32 GM141746NIMH NIH HHS R01 MH121079NIMH NIH HHS R01 MH126137
6 · The paper itself

Abstract

In this paper, we propose a novel deep neural network (DNN) architecture with fractal structure and attention blocks. The new method is tested to identify and segment 2D and 3D brain tumor masks in normal and pathological neuroimaging data. To circumvent the problem of limited 3D volumetric datasets with raw and ground truth tumor masks, we utilized data augmentation using affine transformations to significantly expand the training data prior to estimating the network model parameters. The proposed

Indexed as

attention blocksbrain tumor segmentationdata augmentationfractal neural networkneural networksUnet

Identifiers

PMID39734588
PMCPMC11671157

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.