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Record W2346855685 · doi:10.4103/2153-3539.181770

Evaluation of panoramic digital images using Panoptiq for frozen section diagnosis

2016· article· en· W2346855685 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Pathology Informatics · 2016
Typearticle
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsnot available
Fundersnot available
KeywordsSection (typography)Computer scienceFrozen section procedureComputer graphics (images)Digital pathologyInformation retrievalData miningArtificial intelligenceMedicinePathologyOperating system

Abstract

fetched live from OpenAlex

INTRODUCTION: Whole slide imaging (WSI) permits intraoperative consultations (frozen sections) to be performed remotely. However, WSI files are large and can be problematic if there are tissue artifacts (e.g., tissue folds) or when slides are scanned without multiplanes (Z-stacks) to permit focusing. The Panoptiq dynamic imaging system allows users to create their own digital files that combine low power panoramic digital images with regions of interest that can be imaged using high power Z-stacks. The aim of this study was to determine the utility of the Panoptiq dynamic imaging system for frozen section telepathology. MATERIALS AND METHODS: Twenty archival randomly selected genitourinary surgical pathology frozen sectional cases were evaluated using conventional light microscopy (glass slides), panoramic images, and whole slide images. To create panoramic images glass slides were digitized using a Prosilica GT camera (model GT1920C, Allied Vision Technologies) attached to an Olympus B × 45 microscope and Dell Precision Tower 810 computer (Dell). Panoptiq 3 version 3.1.2 software was used for image acquisition and Panoptiq View version 3.1.2 to view images (ViewsIQ, Richmond, BC, Canada). Image acquisition using Panoptiq software involved a pathology resident, who manually created digital maps (×4 objective) and then selected representative regions of interest to generate Z-stacks at higher magnification (×40 objective). Whole slide images were generated using an Aperio XT Scanscope (Leica) and viewed using ImageScope Software (Aperio ePathology, Leica). Three pathologists were asked to render diagnoses and rate image quality (1-10) and their diagnostic confidence (1-10) for each modality. RESULTS: The diagnostic concordance with glass slides was 98.3% for panoramic images and 100% for WSI. Panoptiq images were comparable to the glass slide viewing experience in terms of image quality and diagnostic confidence. Complaints regarding WSI included poor focus near tissue folds and air bubbles. Panoptiq permitted fine focusing of tissue folds and air bubbles. Issues with panoramic images included difficulty interpreting low-resolution ×4 image maps and the presence of tiling artifacts. In some cases, Z-stacked areas of Panoptiq images were limited or not representative of diagnostic regions. The image file size of Panoptiq was more than 14 times smaller than that of WSI files. CONCLUSIONS: The Panoptiq imaging system is a novel tool that can be used for frozen section telepathology. Panoramic digital images were easy to generate and navigate, of relatively small file size, and offered a mechanism to overcome focusing problems commonly encountered with WSI of frozen sections. However, the acquisition of representative Panoptiq images was operator dependent with the individual creating files that may impact the final diagnosis.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.313
Teacher spread0.267 · 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