Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment (Volume II)

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 2769

Special Issue Editors


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Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
Interests: computational intellgence; neural networks; image processing; expert systems
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Guest Editor
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: data science; machine learning; data structures and algorithms; systems engineering; neural networks; data mining; project management; tensor flow; predictive modelling; artificial intelligence; hadoop; apache spark; software development; empirical researchbig data
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Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of a previous one, entitled “Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment”.

Deep learning is a machine learning method that allows for computational models composed of multiple processing layers to be fed raw data and automatically learn various abstract data representations for detection and classification. New deep learning methods and applications are demanded with advances in medical imaging. Due to considerable variation and complexity, it is necessary to learn the representations of clinical knowledge from big imaging data for a better understanding of health informatics. However, numerous challenges include diverse and inhomogeneous inputs, high-dimensional features versus inadequate subjects, subtle key patterns hidden by sizeable individual variation and, sometimes, an unknown mechanism underlying the disease. These challenges and opportunities are inspiring, and an increasing number of people are devoted to the research direction of machine learning in medical imaging nowadays.

The sudden increase in the market for imaging science enhances opportunities to develop new imaging techniques for diagnosis and therapy. New clinical indications using proteomic or genomic expression in oncology, cardiology and neurology also offer a stimulus. This promotes the growth of procedure volume and sales of clinic imaging agents, and the development of new radiopharmaceuticals. This Special Issue will bring together researchers from diverse fields and specializations, such as healthcare engineering, bioinformatics, medical doctors, computer engineering, computer science, information technology and mathematics.

Potential topics include, but are not limited to:

  • Recent advances in bioimaging applications in preclinical drug discovery;
  • Computer-aided detection and diagnosis;
  • Image analysis of anatomical structures/functions and lesions;
  • Multi-modality fusion for analysis, diagnosis, and intervention;
  • Deep learning for medical applications;
  • Automated medical diagnostics;
  • Advances in imaging instrumentation development;
  • Hybrid imaging modalities in disease management;
  • Medical image reconstruction;
  • Medical image retrieval;
  • Molecular/pathologic/cellular image analysis;
  • Dynamic, functional and physiologic imaging.

Prof. Dr. Marcin Woźniak
Prof. Dr. Muhammad Fazal Ijaz
Guest Editors

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 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. Cancers 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 2900 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

  • deep learning
  • machine learning
  • medical imaging
  • bioimaging
  • computer-aided diagnosis oncology

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Related Special Issue

Published Papers (2 papers)

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11 pages, 2025 KiB  
Article
Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI
by Kazim Z. Gumus, Julien Nicolas, Dheeraj R. Gopireddy, Jose Dolz, Seyed Behzad Jazayeri and Mark Bandyk
Cancers 2024, 16(13), 2348; https://doi.org/10.3390/cancers16132348 - 26 Jun 2024
Cited by 1 | Viewed by 1360
Abstract
Background: Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. Methods: We studied 53 [...] Read more.
Background: Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. Methods: We studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE). Results: The MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI. Conclusions: Compared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric. Full article
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9 pages, 1601 KiB  
Article
Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors—A Multicentric Study
by Miguel Mascarenhas Saraiva, Lucas Spindler, Thiago Manzione, Tiago Ribeiro, Nadia Fathallah, Miguel Martins, Pedro Cardoso, Francisco Mendes, Joana Fernandes, João Ferreira, Guilherme Macedo, Sidney Nadal and Vincent de Parades
Cancers 2024, 16(10), 1909; https://doi.org/10.3390/cancers16101909 - 17 May 2024
Cited by 1 | Viewed by 935
Abstract
High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA [...] Read more.
High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces. Full article
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