The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Deep-learning models have enabled performance leaps in analysis of high-dimensional
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …

Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …

The sole use of single modality data often fails to capture the complex heterogeneity among
patients, including the variability in resistance to anti-HER2 therapy and outcomes of …

Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …

Objective: Breast lesion segmentation in ultrasound images is of great significance for
qualitative breast lesions. However, blurred lesion boundaries, irregular lesion shapes, and …

Training machine learning models for artificial intelligence (AI) applications in pathology
often requires extensive annotation by human experts, but there is little guidance on the …

Diagnosing melanocytic lesions is one of the most challenging areas of pathology with
extensive intra-and inter-observer variability. The gold standard for a diagnosis of invasive …