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Record W3045982619 · doi:10.1364/ol.397223

All-optical label-free human breast tissue block histology using photoacoustic remote sensing

2020· article· en· W3045982619 on OpenAlexafffund
Saad Abbasi, Deepak Dinakaran, Gilbert Bigras, John R. Mackey, Parsin Haji Reza

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsIllumisonics (Canada)University of AlbertaUniversity of Waterloo
FundersCentre for Bioengineering and Biotechnology, University of WaterlooMitacsUniversity of WaterlooillumiSonicsCanada Foundation for InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoacoustic imaging in biomedicineComputer scienceMicroscopeWorkflowBiomedical engineeringPathologyMaterials scienceMedicineOptics

Abstract

fetched live from OpenAlex

The direct imaging of tissue preserved in formalin-fixed paraffin-embedded (FFPE) blocks remains a challenge. There are presently millions of tissues preserved as FFPE blocks whose assessment via bright-field microscopes requires them to be sectioned and subsequently stained. These processes are laborious, resource-intensive, and time consuming. In this Letter, we utilize an ultraviolet laser with photoacoustic remote sensing to provide a novel method that enables direct label-free pathological assessment of FFPE blocks. We demonstrate the efficacy of this technique by imaging human breast tissue, highlighting salient features such as ducts, adipocytes, and ductal hyperplasia. This direct imaging of FFPE blocks facilitates pathological assessment much earlier in the histopathological workflow, saving valuable time in clinical and research settings. The presented non-contact label-free reflection-mode device enables augmentation of existing histopathological workflows and aims to expand the arsenal of imaging technologies available to clinicians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.239
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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