Human-centric Computing and Information Sciences (HCIS)

Special issue on Deep learning-based human-centric biomedical diagnosis: Challenges and Perspectives

Many biomedical practices can be regarded as decision-making. Nowadays, computers have evolved as crucial components in medical decision-making. Still, the general perception of “computers in biomedicine” is often only of computer applications that assist physicians in diagnosing illnesses. The role of the human is weakened. To integrate computers more closely into human experts in the biomedical fields, researchers and practitioners rely on human-centric biomedical diagnosis(HCBD) and its underlying technologies in biomedicine.

The imaging data can be obtained from multiple imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) Imaging, Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging (MPI), EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. Analysis of such modalities has been traditionally conducted with classical statistics, either hypothesis testing or Bayesian inference, relying on frequently violated assumptions. One promising solution is machine learning within knowledge-based systems, where high-dimensional relationships between datasets are empirically established.

This special issue aims to report the newest developments of deep learning-based HCBD in methodologies and applications of the biomedical fields. For example, the explanations for black-box predictions with methods to extract HCBD in brain diseases. Topics of interest include, but are not limited to:

 Theory, models, frameworks, and tools of HCBD
 Trustworthy HCBD models
 HCBD-enabled natural language processing and understanding
 Deep learning-based HCBD models
 Security and privacy related HCBD systems
 HCBD in robotics, social science, and healthcare
 Big data in HCBD systems and their applications
 HCBD -based question answering systems and recommendation systems
 Attention mechanism and explainability in HCBD models
 Smart sensor technologies of HCBD systems
 HCBD for human-machine interaction and collaborations
 HCBD with autonomous deep learning, automated reasoning, and reinforcement learning
 Deep graph networks for HCBD

Important Dates

Open submission: 30 Dec. 2021
Submission deadline: 30 Mar. 2022
Author notification: within 4 weeks after submission
Revised manuscript due: within 2 weeks after notification
Notification of acceptance: within 2 weeks after revision submission
Tentative accepted paper publication date: within 2 months after final version
Tentative SI paper collection and its web open: 4th Quarter, 2022 (TBA)

Submission Guideline

All submitted papers must be clearly written in excellent English and contain only original work. All papers must be submitted in an electronic format, e.g., PDF format (preferred) or MS Word. Manuscripts should follow the formatting of the sample manuscript and references. You can refer to the details in the submission menu http://hcisj.com/submission/preparing_manuscript.php All papers and some supplementary materials should be submitted through ScholarOne Manuscripts. The authors must select “SI2021-08DL-HCBiomedicalDiagnosis”. when they reach the “Article Type” step in the submission process https://mc04.manuscriptcentral.com/hcis