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Applied Sciences
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Diagnosis and Analysis of Cancer Diseases
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Diagnosis and Analysis of Cancer Diseases

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 2821

Special Issue Editor

Prof. Dr. Zhiqiong Wang

E-Mail Website
Guest Editor
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
Interests: computer-aided diagnosis; machine learning; healthcare blockchain; biological data processing

Special Issue Information

Dear Colleagues,

Early diagnosis and deep analysis of cancer diseases can help improve the cure rate and quality of life for patients with these diseases, which is an important research field. Using artificial intelligence, big data technology and computer-aided diagnosis to carry out early diagnosis of cancer diseases based on medical images, including focus area detection, segmentation, extraction and classification of benign and malignant, can help clinical find diseases earlier for treatment and reduce misdiagnosis rate. The medical data analysis method based on bioinformatics, biological data mining and graph theory can help us to understand the occurrence, development, pathogenesis and mechanism of cancer diseases and provide new solutions for disease diagnosis, clinical treatment and drug research and development. Therefore, this Special Issue welcomes excellent research papers and review articles and devotes itself to discussing the frontier research and latest progress in the diagnosis and analysis of cancer diseases on medical images and data to provide new measures and ideas for the treatment of cancer patients and fundamentally reduce the burden and pain of patients.

For this Special Issue, topics of interest include, but are not limited to:

  • Computer-aided diagnosis
  • Machine learning
  • Deep learning
  • Distributed computing
  • Transfer learning
  • Data mining
  • Correlation analysis
  • Big data processing
  • Medical image
  • Biological data
  • Medical text data

Prof. Dr. Zhiqiong Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cancer diseases
  • artificial intelligence
  • diagnosis and analysis

Published Papers (2 papers)

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Research

14 pages, 1584 KiB  
Article
Three-Dimensional Multifaceted Attention Encoder–Decoder Networks for Pulmonary Nodule Detection
by Keyan Cao, Hangbo Tao and Zhongyang Wang
Appl. Sci. 2023, 13(19), 10822; https://doi.org/10.3390/app131910822 - 29 Sep 2023
Cited by 1 | Viewed by 754
Abstract
Lung cancer is one of the most dangerous cancers in the world, and its early clinical manifestation is malignant nodules in the lungs, so nodule detection in the lungs can provide the basis for the prevention and treatment of lung cancer. In recent [...] Read more.
Lung cancer is one of the most dangerous cancers in the world, and its early clinical manifestation is malignant nodules in the lungs, so nodule detection in the lungs can provide the basis for the prevention and treatment of lung cancer. In recent years, the development of neural networks has provided a new paradigm for creating computer-aided systems for pulmonary nodule detection. Currently, the mainstream pulmonary nodule detection models are based on convolutional neural networks (CNN); however, as the output of a CNN is based on a fixed-size convolutional kernel, it can lead to a model that cannot establish an effective long-range dependence and can only model local features of CT images. The self-attention block in the traditional transformer structures, although able to establish long-range dependence, are as ineffective as CNN structures in dealing with irregular lesions of nodules. To overcome these problems, this paper combines the self-attention block with the learnable regional attention block to form the multifaceted attention block, which enables the model to establish a more effective long-term dependence based on the characteristics of pulmonary nodules. And the multifaceted attention block is intermingled with the encoder–decoder structure in the CNN to propose the 3D multifaceted attention encoder–decoder network (MAED), which is able to model CT images locally while establishing effective long-term dependencies. In addition, we design a multiscale module to extract the features of pulmonary nodules at different scales and use a focal loss function to reduce the false alarm rate. We evaluated the proposed model on the large-scale public dataset LUNA16, with an average sensitivity of 89.1% across the seven predefined FPs/scan criteria. The experimental results show that the MAED model is able to simultaneously achieve efficient detection of pulmonary nodules and filtering of false positive nodules. Full article
(This article belongs to the Special Issue Diagnosis and Analysis of Cancer Diseases)
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17 pages, 1193 KiB  
Article
Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer’s Disease
by Zhiqiong Wang, Zican Lin, Shuo Li, Yibo Wang, Weiying Zhong, Xinlei Wang and Junchang Xin
Appl. Sci. 2023, 13(14), 8433; https://doi.org/10.3390/app13148433 - 21 Jul 2023
Viewed by 1429
Abstract
Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer’s disease diagnosis, we propose a new network [...] Read more.
Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer’s disease diagnosis, we propose a new network architecture called Dynamic Multi-Task Graph Isomorphism Network (DMT-GIN). This approach uses fMRI images transformed into brain network structures to classify Alzheimer’s disease more effectively. In the DMT-GIN architecture, we integrate an attention mechanism with the Graph Isomorphism Network (GIN) to capture node features and topological structure information. To further enhance AD classification performance, we incorporate auxiliary tasks of gender and age classification prediction alongside the primary AD classification task in the network. This is achieved through sharing network parameters and adaptive weight adjustments for simultaneous task optimization. Additionally, we introduce a method called GradNorm for dynamically balancing gradient updates between tasks. Evaluation results demonstrate that the DMT-GIN model outperforms existing baseline methods on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, leading in various metrics with a prediction accuracy of 90.44%. This indicates that our DMT-GIN model effectively captures brain network features, providing a powerful auxiliary means for the early diagnosis of Alzheimer’s disease. Full article
(This article belongs to the Special Issue Diagnosis and Analysis of Cancer Diseases)
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