Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation.

Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. 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

6 authors.

Stephanie BatistaPolytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal.ORCID 0000-0002-7352-5495

Miguel CouceiroPolytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal.ORCID 0000-0002-6800-8653

Ricardo FilipeAltice Labs, S.A, 3810-106 Aveiro, Portugal.ORCID 0000-0003-4438-4176

Paulo RachinhasCoimbra Hospital and University Center, 3004-561 Coimbra, Portugal.

Jorge IsidoroCoimbra Hospital and University Center, 3004-561 Coimbra, Portugal.ORCID 0000-0003-4402-6918

Inês DominguesPolytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal.ORCID 0000-0002-2334-7280

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a crucial task for diagnosing and treating liver diseases. However, these models often face challenges in adapting to diverse clinical data sources as differences in dataset volume, resolution, and origin impact generalization and performance. This study introduces a

Indexed as

artificial neural networksautomatic liver segmentationBerDiffcomputed tomographyFCNprivate data incrementalizationResUNet++U-Net

Identifiers

PMID40428149

DOI10.3390/bioengineering12050530

PMCPMC12108710

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.