Min Tang

Jialiang Yang

Li Xiao

Thanks to the development and deployment of whole-slide imaging technology in pathology, glass slides previously observed under a traditional microscope are now scanned and converted to digital images, which are more beneficial for remote access, portability, and ease of sharing to facilitate telepathology. More importantly, digitization of glass slides paves the way towards the wide use of artificial intelligence (AI) tools including machine/deep learning algorithms, resulting in improved diagnostic accuracy. In the past decade, a large number of studies have demonstrated the remarkable success of AI, particularly deep learning, in digital pathology, such as tumor region identification, metastasis detection, and patient prognosis. Differing from handcrafted feature-based approaches that take advantage of domain knowledge to delineate specific morphological measurements (e.g., nuclei shape and size and tissue texture) in the images as features for training, deep learning is a paradigm of feature learning entirely driven by the image data and/or labels. Herein, the use of deep learning in pathological diagnosis can not only handle increased workloads and expertise shortages but also obviate subjective diagnosis from pathologists. Yet there remain many scientific and technological challenges associated with the efficiency of deep learning algorithms for use in clinical practice. For example, deep learning requires a sufficient amount of training data for generalization and suffers from a lack of feature interpretability. The overarching goal of this special issue is to highlight novel research accomplishments and directions, related to advanced AI methodology development and applications in digital pathology.

Artificial Intelligence in Digital Pathology Image Analysis

Artificial intelligence in digital pathology image analysis

Yifei Xu

Liao Bo

Tuo Zhang

Bioinformatics Analysis of Single Cell Sequencing Data and Applications in Precision Medicine

Cheng Guo

Ling Kui

Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume III

Shuai Cheng Li

Computational Methods in Inferring Cancer Tissue-of-Origin and Cancer Molecular Classification, Volume I

Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume II

Tao Huang

Yudong Cai

Minxian Wallace Wang

Computational Methods in Predicting Complex Disease Associated Genes and Environmental Factors

Jialiang Yang

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