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Record W4249058857 · doi:10.1117/3.651880.ch3

An Indexed Atlas of Digital Mammograms for Computer-Aided Diagnosis of Breast Cancer

2010· book-chapter· en· W4249058857 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

VenueSPIE eBooks · 2010
Typebook-chapter
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMammographyBreast cancerRadiologyMalignancyContext (archaeology)Digital mammographyAbnormalityBreast cancer screeningAsymptomaticMagnetic resonance imagingMedical physicsCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

Screening mammography is used for the early detection of breast cancer in asymptomatic women (between the ages of 50 and 69 in Canada). Screen Test: Alberta Program for the Early Detection of Breast Cancer has been in operation since 1990, and attracts the participation of over 21,000 women per year. Screen Test has offices in Calgary and Edmonton, and offers extended service with three mobile units to more than 100 sites in the province of Alberta. Mammograms are difficult images to interpret, especially in the screening context where the probability of encountering an abnormality is low and patient information is limited. A cost-effective, efficient method needs to be developed in order to achieve high diagnostic accuracy. Diagnostic mammography refers to the radiological examination of symptomatic women who exhibit clinical signs such as a palpable lump, skin puckering, or nipple retraction, or as a result of screening. Mammography is used to detect abnormalities and classify them as benign or malignant. Ambiguous cases with suspicious features detected on mammograms are evaluated further with adjunctive imaging procedures. Depending on the characteristics of the abnormality, these procedures may include supplementary views, ultrasound, magnification mammography, magnetic resonance imaging, computed tomography, and nuclear medicine techniques. Biopsy is indicated if these methods do not lead to a definite diagnosis but indicate a high suspicion for malignancy, and confirmation of malignancy is required. Objective methods for the analysis of mammographic features are needed for the development of computer methods to assist radiologists in the evaluation of ambiguous features; that is, for computer-aided diagnosis (CAD) of breast cancer. Current research is directed toward the development of digital mammographic imaging and image analysis systems that can detect features, classify them, and give visual prompts to the radiologist, such as the Image Checker by R2 Technology, EasyVision RAD used with Philips' Computed Radiography system, and Second Look from iCAD.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.253
Teacher spread0.235 · 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