Convolutional neural network (CNN) has promoted the development of diagnosis
technology of medical images. However, the performance of CNN is limited by insufficient …

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital
pathology is essential for assessing prognosis. Due to the increasing resolution and quantity …

Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …

Learning underlying patterns from sensory data is crucial in the Human Activity Recognition
(HAR) task to avoid poor generalization when coping with unseen data. A key solution to …

Automatic classification of colon and lung cancer images is crucial for early detection and
accurate diagnostics. However, there is room for improvement to enhance accuracy …

Kidney cancer is the most common type of cancer, and designing an automated system to
accurately classify the cancer grade is of paramount importance for a better prognosis of the …

Fisher discriminant analysis (FDA) is a widely used dimensionality reduction tool in pattern
recognition. However, FDA cannot obtain an optimal subspace for classification without …

We aimed to develop deep learning (DL) models to detect protein expression in
immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and …

Affect Sensing is a rapidly growing field with the potential to revolutionize human–computer
interaction, healthcare, and many more applications. Multimodal Sentiment Analysis (MSA) …

Vascular images contain a lot of key information, such as length, diameter and distribution.
Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the …