This paper proposes BCA-YOLO, a YOLO-based architecture enhanced with three novel components. Bilateral Cross-Attention (BCA) block, which computes cross-attention between the feature maps of the left breast and the horizontally mirrored contralateral right-breast feature maps at each feature scale. It then introduces an asymmetry-based residual update controlled by a learnable scalar coefficient α. Inter-View Consistency (IVC) module, which integrates craniocaudal (CC) and mediolateral oblique (MLO) features for each side using a learnable channel-wise gating mechanism. Ordinal BI-RADS regression head, which replaces conventional categorical cross-entropy with a cumulative link model employing monotonically parameterized thresholds. A reference implementation of the BCA-YOLO head in PyTorch adds approximately 3.1 million trainable parameters to the YOLOv8/v11 backbone. The BCA module is initialized with α = 0, allowing the model to exactly reproduce the baseline model for each mammographic view at initialization while preserving compatibility with pretrained backbone networks.
| Mualliflar | Туракулов, Ш.Х. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2026-06-25 |
| Jild | 9 |
| Son | 2 |
| Betlar | 143-149 |
| Til | Rus |
| DOI | 10.62132/ijdt.v9i2.389 |
DOI: 10.62132/ijdt.v9i2.389 · Maqolaning asl sahifasi
маммография, обнаружение объектов, двусторонняя асимметрия, перекрёстное внимание, ординальная регрессия, YOLO, глубокое обучение, mammography, object detection, bilateral asymmetry, cross-attention, ordinal regression, YOLO, deep learning
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