Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring
various diseases. Convolutional neural networks (CNNs) have become popular as they can …

Diffusion models have enabled remarkably high-quality medical image generation, yet it is
challenging to enforce anatomical constraints in generated images. To this end, we propose …

Purpose: Medical images acquired using different scanners and protocols can differ
substantially in their appearance. This phenomenon, scanner domain shift, can result in a …

The image acquisition parameters (IAPs) used to create MRI scans are central to defining
the appearance of the images. Deep learning models trained on data acquired using certain …

Shifts in data distribution can substantially harm the performance of clinical AI models.
Hence, various methods have been developed to detect the presence of such shifts at …

This paper presents an effective learning multi-spike deep spiking neural network with
temporal feedback backpropagation for breast cancer detection using contrast-enhanced …

Determining whether two sets of images belong to the same or different domain is a crucial
task in modern medical image analysis and deep learning, where domain shift is a common …

Infrared imagery is indispensable for capturing temperature data by detecting infrared
radiation, particularly in challenging environments characterized by low-light conditions …

With the increasing demand for personalized clothing from consumers, the style transfer
technology of clothing images has become a key link in clothing customization design …