Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray
images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear …

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the
refereed proceedings of the 22nd International Conference on Medical Image Computing …

Hip and pelvic fractures are serious injuries with life-threatening complications. However,
diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the demand …

The interest in artificial intelligence (AI) has ballooned within radiology in the past few years
primarily due to notable successes of deep learning. With the advances brought by deep …

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide
incidence projected to increase by 240–310% by 2050. Hip fractures are predominantly …

Proximal femur fractures represent a major health concern, and substantially contribute to
the morbidity of elderly. Correct classification and diagnosis of hip fractures has a significant …

Convolutional Neural Networks (CNNs) became the de-facto standard for medical image
analysis. In CNN, pooling layers are used for downsampling feature maps by aggregating …

The increased rate of Total Hip Replacement (THR) for relieving hip pain and improving the
quality of life has been accompanied by a rise in associated post-operative complications …

Deep Learning methods over the past years provided high-performance solutions for the
medical applications. Yet, robustness and quality control is still required for clinical …