Automated classification of liver lesions in multi-phase CT and MR scans is of clinical
significance but challenging. This study proposes a novel Siamese Dual-Resolution …

In recent years, medical image classification techniques based on deep learning have made
remarkable achievements, but most of the current models sacrifice the efficiency of the …

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects
within images. However, traditional algorithms encounter difficulties when detecting small …

Convolutional neural network (CNN)-based object detectors perform excellently but lack
global feature extraction and cannot establish global dependencies between object pixels …

Masked image modeling (MIM) has made significant advancements across various fields in
recent years. Previous research in the hyperspectral (HS) domain often utilizes conventional …

Crack detection methods using deep learning models such as convolutional neural network
(CNN) and the newly developed vision transformer (ViT) are expanding. However, there is …

In the specialized field of renal histology, precise segmentation of glomeruli in microscopic
images is crucial for accurate clinical diagnosis and pathological analysis. Facing the …

The You Only Look Once (YOLO) series of object detection models is widely recognized for
its efficiency and real-time performance, particularly under the challenging conditions of …

Risk identification in power operations is crucial for both personal safety and power
production. Existing risk identification methods mainly use target detection models to identify …

In the real-world scenarios, challenges such as changes in viewpoint, variations in lighting
conditions, and the presence of blur effects are pervasive issues faced in the field of image …