State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images
Bibliographic record
Abstract
The prostate-specific membrane antigen (PSMA) is a powerful target for positron emission tomography (PET) that has opened a new era in the diagnosis and management of prostate cancer (PCa). Aiming to provide an automated diagnostic and management tool that can help detect metastatic PCa lesions in PSMA-PET images, we deployed and investigated an array of state-of-the-art deep learning-based object detection algorithms (4 categories of multi-stage, single-stage, anchor-free, and end-to-end transformer-based). The results of 17 trained networks are reported in terms of 3 metrics (precision, recall, and F1 score), showing the ability of object detection models to localize PCa metastatic lesions of different sizes and standard uptake values (SUV). Our goal is to provide a fully automated computer-aided diagnosis (CAD) tool to assist physicians in performing the diagnosis by significantly saving time and decreasing false-negative rates. A novelty of the present work is to focus on multiple rotations of maximum intensity projection (MIP) images computed on 3D volumes in the dataset, as a new investigative training framework for detection. .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".