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Diagnostics
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Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders...
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Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders 2.0

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1987

Special Issue Editors

Dr. Barbara Palumbo

E-Mail Website
Guest Editor
Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Perugia, Italy
Interests: nuclear medicine; image-based diagnostics; artificial intelligence; PET/CT; SPECT; SPECT/CT; radiomics; oncology; neurodegenerative disorders
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Angela Spanu

E-Mail Website
Guest Editor
Nuclear Medicine Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, Italy
Interests: nuclear medicine; image-based diagnostics; SPECT; SPECT/CT; PET/CT; molecular breast imaging; oncology (breast cancer, lung cancer, thyroid cancer, neuroendocrine tumors, and prostate cancer); radiomics; neurodegenerative disorders; radiometabolic therapy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luca Brunese

E-Mail Website
Guest Editor
Department of Medicine and Health Sciences “Vincenzo Tiberio”, Università degli Studi del Molise, Campobasso, Italy
Interests: artificial intelligence; image-based diagnostics; CT; MRI; radiology; radiomics; oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Bianconi

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti, 93-06125 Perugia, Italy
Interests: artificial intelligence; computational imaging; computer vision; image processing; medical image analysis; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Diagnostic imaging has undergone major changes in recent years. Radiological and nuclear medicine imaging play a pivotal role in the diagnosis, stratification and follow-up of oncological and neurological disorders. The improving capabilities of the imaging devices and the increasing availability of storing, sharing and computing facilities have been generating larger and larger amounts of data. Consequently, there has been increasing attention on the development of computational methods for the extraction of objective imaging features (biomarkers), capable of correlating with disease phenotype, clinical outcome and/or response to treatment. The combined use of imaging data, biomarkers and artificial intelligence makes it possible to build powerful predictive models, which can assist the physician in the management of patients with a wide range of disorders, particularly oncological and neurological, ultimately leading to personalized treatment and better clinical outcome. However, there are still open challenges before these methods can be translated into clinical practice. Critical to this process, for instance, are standardization, man–machine interaction, strong interdisciplinary cooperation and the availability of centralized repositories of annotated data.  

This Special Issue aims to provide a forum to discuss challenges, discoveries and opportunities in the field, with specific focus on the diagnosis of oncological and neurological disorders via radiological and nuclear medicine modalities. We encourage the submission of research papers as well as review articles; comparative evaluations and new datasets are also welcome.

Dr. Barbara Palumbo
Prof. Dr. Angela Spanu
Prof. Dr. Luca Brunese
Dr. Francesco Bianconi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Diagnostics 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 2600 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

  • artificial intelligence in diagnostic imaging
  • computer-assisted diagnosis and prognostication
  • data mining and big data
  • deep learning
  • image processing (including acquisition, pre-processing, segmentation and feature extraction)
  • radiology
  • radiomics
  • nuclear medicine
  • imaging modalities (including CT, MRI, PET, PET/CT, PET/MRI, SPECT, SPECT/CT)
  • oncological and neurological disorders
  • personalized medicine

Published Papers (1 paper)

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Research

14 pages, 5412 KiB  
Article
Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images
by Chengzhang Zhu, **an Chai, Yalong **ao, Xu Liu, Renmao Zhang, Zhangzheng Yang and Zhiyuan Wang
Diagnostics 2024, 14(3), 269; https://doi.org/10.3390/diagnostics14030269 - 26 Jan 2024
Viewed by 1604
Abstract
Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with [...] Read more.
Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders 2.0)
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