Mammographic lesion detection based on BCA-YOLO and BI-RADS regression

Туракулов, Ш.Х.

Рақамли технологияларнинг назарий ва амалий масалалари · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarТуракулов, Ш.Х.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
Nashr sanasi2026-06-25
Jild9
Son2
Betlar143-149
TilRus
DOI10.62132/ijdt.v9i2.389

Kalit so‘zlar

маммография, обнаружение объектов, двусторонняя асимметрия, перекрёстное внимание, ординальная регрессия, YOLO, глубокое обучение, mammography, object detection, bilateral asymmetry, cross-attention, ordinal regression, YOLO, deep learning

Ilmiy soha

Рақамли технологияларнинг назарий ва амалий масалалари jurnalidan boshqa maqolalar

Рақамли технологияларнинг назарий ва амалий масалалари — barcha maqolalar