Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma.

Shenghui Yan, Sidong Liu, Antonio Di Ieva, Maurice Pagnucco, Yang Song

Abstract readReview

In one paragraph

Review in Advances in experimental medicine and biology, 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

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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

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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

5 authors.

Shenghui YanSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.

Sidong LiuCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.

Antonio Di IevaComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.

Maurice PagnuccoSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.

Yang SongSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia. yang.song1@unsw.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, numerous algorithms have emerged for the segmentation of brain tumors, propelled by both the advancements of deep learning techniques and the influential open benchmark set by the BraTS challenge. This chapter provides an overview of the background that gave rise to automated brain tumor segmentation algorithms, reviews representative deep learning-based approaches, and reflects their limits on clinical applicability. While these algorithms showcase promising results in fully supervised settings, they may not perform well to other types of brain tumors without substantial samples for model re-training or fine-tuning. Recognizing this limitation, we explore a new learning framework designed to facilitate fast adaptation to new tumor types with only a few labeled data samples.

Indexed as

Brain NeoplasmsDeep LearningGliomaAlgorithmsHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingBrain tumorDeep learningFew-shot learningImage segmentationMeta-transfer learning

Identifiers

PMID39523268

DOI10.1109/ISBI.2018.8363678

What OpenQuestion holds

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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.