Computer Vision in Digital Neuropathology.

Cong Cong, Sidong Liu, Antonio Di Ieva, Carlo Russo, Eric Suero Molina, 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

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

7 authors.

Cong CongSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia. thomas.cong@mq.edu.au.

Sidong LiuCentre for Health Informatics, Macquarie University, Sydney, NSW, Australia.

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

Carlo RussoComputational NeuroSurgery Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.

Eric Suero MolinaComputational NeuroSurgery 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital pathology has revolutionized the field of neuropathology, enabling rapid and precise analysis of tissue samples. As neuropathologists and neurosurgeons increasingly embrace digital platforms, computer vision techniques and computerized quantitative analyses (i.e., pathomics) play a pivotal role in automating and enhancing diagnostic processes, as well as in unlocking new insights from neuropathological images. This chapter explores the role of computer vision in bridging the gap between traditional and digital neuropathology, providing a comprehensive overview of computer vision applications in neuropathology, covering image preprocessing, and decision support for clinical tasks, such as early detection of neurological disorders, classification of brain tumors, and quantitative assessment of tissue biomarkers. In addition, we further delve into the challenges and opportunities presented by large-scale image datasets and the integration of digital pathology into neurosurgical practice.

Indexed as

NeuropathologyBrain NeoplasmsHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedNervous System DiseasesComputational histopathologyComputational neuropathologyComputer visionDigital neuropathologyImage classificationPathomicsStain normalization

Identifiers

PMID39523263

DOI10.1016/j.future.2022.03.009

What OpenQuestion holds

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Registered trials

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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.