In recent years, the integration of deep learning techniques with biophotonic setups has
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …

Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …

Head and neck tumors are the sixth most common neoplasms. Multiomics integrates
multiple dimensions of clinical, pathologic, radiological, and biological data and has the …

Microscopic image analysis plays a significant role in initial leukemia screening and its
efficient diagnostics. Since the present conventional methodologies partly rely on manual …

Weakly supervised semantic segmentation under image-level annotations is effectiveness
for real-world applications. The small and sparse discriminative regions obtained from an …

Image segmentation tasks based on deep learning usually require a large number of
labeled samples to obtain great performance of Convolutional Neural Networks (CNNs) …

The rapid development of healthcare big data has brought certain convenience to medical
research and health management, but privacy protection of healthcare big data is an issue …

Depression is a mental disorder characterized by persistent depressed mood or loss of
interest in performing activities, causing significant impairment in daily routine. Possible …

Plant diseases pose a significant threat to agricultural productivity, emphasizing the
essential need for early detection and diagnosis. While recent deep learning approaches …

With the development of sensors, more and more multimodal data are accumulated,
especially in biomedical and bioinformatics fields. Therefore, multimodal data analysis …