Enhancing Breast Cancer Diagnosis: A Semi-Supervised Deep Learning Model for Automated MRI Tumor Segmentation
Bibliographic record
Abstract
The upsurge in the number of breast cancer patients makes it important to diagnose the disease early so that effective treatment can save the patient's life.Breast cancer diagnosis is challenging; however, the adoption of various deep-learning techniques has made this hard work much more accessible for radiologists to diagnose breast cancer at an early stage.Numerous applications have been developed to provide practical solutions to aid the radiologist in medical image analysis.Magnetic Resonance Imaging (MRI) is considered the most accurate screening technique for breast cancer identification, and it has substantially contributed to decreasing the mortality rate by early breast cancer detection.In MRI imaging, breast tumor detection and segmentation are still regarded as a critical task due to the limited availability of annotated MRI scans that require more laborious to annotate data with accurate ground truth which is a time-consuming process and less feasible in medical imaging.This research presents a Semi-Supervised Deep Learning for Automated Tumor Segmentation (SDATS).A semi-supervised learning model is employed, utilizing labeled and unlabeled data for segmentation.Segment Anything Model (SAM) is used for tumors, using bounding boxes to isolate regions of interest and generating precise segmentation masks.The YOLOv8 model is utilized for breast tumor detection, identifying bounding boxes for regions of interest.Integrating YOLOv8 and SAM makes the proposed model more rigorous and aims to enhance efficiency without using pixel-level annotation for segmentation.This allows for more efficient processing of large datasets and accelerates the diagnostic process.Furthermore, the SDATS diagnoses breast cancer with quicker and more precise automatic segmentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".