Hyperspectral imaging enables many versatile applications for its competence in capturing
abundant spatial and spectral information, which is crucial for identifying substances …

Histopathology is diagnosis based on visual examination of tissue sections under a
microscope. With the growing number of digitally scanned tissue slide images, computer …

Abstract Convolutional Neural Networks (CNN) are state-of-the-art models for many image
classification tasks. However, to recognize cancer subtypes automatically, training a CNN on …

RNA-based, multi-gene molecular assays are available and widely used for patients with ER-
positive/HER2-negative breast cancers. However, RNA-based genomic tests can be costly …

Despite the proven efficacy of hyperspectral imaging in many computer vision tasks, its
widespread use is hindered by its low spatial resolution, resulting from hardware limitations …

Human age provides key demographic information. It is also considered as an important soft
biometric trait for human identification or search. Compared to other pattern recognition …

Automated digital histopathology image segmentation is an important task to help
pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer …

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning
the pre-learned base knowledge towards tasks with considerably smaller data scale are …

The microscopic image of a specimen in the absence of staining appears colorless and
textureless. Therefore, microscopic inspection of tissue requires chemical staining to create …

Recent years has witnessed growing interest in hyperspectral image (HSI) processing. In
practice, however, HSIs always suffer from huge data size and mass of redundant …