Lung disease analysis in chest X-rays (CXR) using deep learning presents significant
challenges due to the wide variation in lung appearance caused by disease progression …

Precise delineation of skin lesions from dermoscopy pictures is crucial for enhancing the
quantitative analysis of melanoma. However, this remains a difficult endeavor due to …

Accurate segmentation of hematoxylin-eosin (H&E) muscle fiber images is crucial for the
diagnosis of weightless muscle atrophy. However, uneven contrast, blurred fiber boundaries …

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can
improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images …

CNN plays a significant role in medical image segmentation. However, many models are
frequently constrained by the receptive field size of convolution kernels, which hinders their …

Existing studies of multi-modality medical image segmentation tend to aggregate all
modalities without discrimination and employ multiple symmetric encoders or decoders for …

Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and
disease diagnosis to inform treatment and intervention. In performing segmentation, deep …

Automatic segmentation of jaw cysts in cone-beam computed tomography (CBCT) scans
plays a crucial role in clinical and treatment planning, and it provides an efficient alternative …

Hybrid CNN-Transformer models are designed to combine the advantages of Convolutional
Neural Networks (CNNs) and Transformers to efficiently model both local information and …

Recently, CNN-based and Transformer-based network have become the de-facto standard
for vessel segmentation, due to their strong feature representation capabilities. However …