Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

Machine learning (ML) can expedite directed evolution by allowing researchers to move
expensive experimental screens in silico. Gathering sequence-function data for training ML …

Abstract Human Activity Recognition (HAR) is an essential task in various applications such
as pervasive healthcare, smart environment, and security and surveillance. The need to …

Abstract We present ProteinShake, a Python software package that simplifies
datasetcreation and model evaluation for deep learning on protein structures. Users …

In recent years, many applications are using various forms of deep learning models. Such
methods are usually based on traditional learning paradigms requiring the consistency of …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Multi-focus image fusion (MFIF) attempts to achieve an “all-focused” image from multiple
source images with the same scene but different focused objects. Given the lack of multi …