LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images.

CSA-SegNet: enhancing ovarian tumor segmentation from ultrasound images with spatial pooling and contour-based loss functions

MKSFA-Net: a integrating structural priors for breast cancer segmentation based on multi-scale kernel selection fusion attention network

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation. To address these limitations, we propose BiLoG-Net, a deep learning framework that jointly performs breast mass segmentation and malignancy classification through bi-context location-aware feature modeling and segmentation-guided attention mechanisms. Our architecture integrates a novel encoder-decoder paradigm with Fire-based feature extraction, lightweight global and local feature enhancement modules, and adaptive location-aware gating to simultaneously capture long-range contextual dependencies and fine-grained boundary-sensitive details. Unlike conventional multi-stage pipelines, our tightly coupled multi-task design enables mutual reinforcement between pixel-level localization and image-level diagnosis, reducing error propagation while producing spatially grounded malignancy predictions. Evaluated on CBIS-DDSM and INBreast benchmarks, BiLoG-Net achieves state-of-the-art performance with Dice scores of 94.20% and 93.10%, classification accuracies of 95.20% and 93.60%, and AUC values of 97.10% and 96.00%, respectively, substantially outperforming existing CNN and transformer-based baselines. By combining precise boundary delineation with reliable malignancy assessment in a single end-to-end model, this work holds strong potential for clinical computer-aided detection systems, helping radiologists prioritize suspicious cases and improve screening efficiency in busy clinical settings.

REC-UNet: a 2D U-Net model with residual cross-dimensional attention for liver tumor segmentation

A novel residual “Enhancement-Calibration” U-Net architecture, termed REC-UNet, which achieves high overall segmentation accuracy across diverse lesion sizes and contrast conditions without relying on explicit size-stratified optimization.

MGT–UNet: a hybrid CNN–transformer network with multi-scale feature learning and global context modeling for breast ultrasound segmentation

Accurate breast ultrasound image segmentation remains challenging because speckle noise, low contrast, and heterogeneous lesion appearance often degrade lesion boundary delineation. Convolutional neural networks provide effective local feature extraction but have limited capability for modeling long-range contextual information. Although transformer-based architectures improve global context modeling, effectively combining local multi-scale representations with global contextual information remains challenging. We propose MGT–UNet, a hybrid CNN–Transformer segmentation network that integrates multi-scale feature learning with global context modeling for breast ultrasound image segmentation. The encoder employs a Tri-Scale Context Extractor to learn hierarchical multi-scale representations, while a Global Context Transformer models long-range contextual dependencies at the bottleneck. The network was evaluated primarily on the BUSI breast ultrasound dataset using Dice coefficient, Intersection-over-Union, Hausdorff Distance at the 95th percentile, Precision, and Recall. Supplementary evaluations were conducted on the ISIC2018 dermoscopic dataset and a binary variant of the Synapse computed tomography dataset using the same segmentation protocol. To ensure reliability, performance metrics were averaged over multiple independent training runs with different random seeds. On the BUSI dataset, MGT–UNet achieved the highest Dice coefficient of 77.74%, outperforming representative CNN-, Transformer-, and hybrid segmentation models while also achieving the lowest HD $$_{95}$$ . The results indicate improved segmentation performance on the BUSI dataset, which contains images affected by speckle noise and weak boundary contrast. Evaluations on dermoscopic and computed tomography datasets complement these findings by examining the behavior of the proposed architecture across diverse modalities. Our results suggest that combining multi-scale feature learning with transformer-based global context modeling is beneficial for breast ultrasound segmentation. The proposed architecture provides a basis for further investigation of hybrid CNN–Transformer models in medical image segmentation.

HCDU-net and the shape of precision: advancing liver tumor segmentation with statistical geometry and adaptive deep learning using U-net