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Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging
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Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 21540

Special Issue Editors

Dr. Zongyuan Ge

E-Mail Website
Guest Editor
eResearch Center, Monash University, Clayton, VIC 3800, Australia
Interests: medical artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Donghao Zhang

E-Mail Website
Guest Editor
eResearch Center, Monash University, Clayton, VIC 3800, Australia
Interests: medical artificial intelligence; medical imaging understanding; diagnosis artificial intelligence; predication medical artificial intelligence
Dr. Deval Mehta

E-Mail Website
Guest Editor
Monash eResearch Center, Monash University, Clayton, VIC 3800, Australia
Interests: deep learning for medical imaging applications; disease detection and classification; openset recognition for medical data
Dr. Dwarikanath Mahapatra

E-Mail Website
Guest Editor
Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
Interests: deep learning for medical image analysis; AI explainability; computer aided detection

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) show that computers have the ability to accomplish tasks that are normally completed by intelligent beings such as humans and animals. Among current AI applications, machine learning (ML) is a tool that combines computer science with statistics for generating advanced algorithms capable of identifying the complex relationships within large datasets. At present, some of the greatest successes of machine learning have been in the field of vision and neural language understanding. Many tasks such as object classification, detection, and segmentation have demonstrated superhuman performances.

Medicine and healthcare, even from the early time of intelligence system research, has been one of the most promising and inspiring domains for the application of automatic decision-making approaches. On the other hand, it has been one of the most challenging areas for effective adoption. AI is transforming healthcare in various domains such as oncology, dermatology, ophthalmology, and radiology. Medical imaging modalities such as EEG, ECG, PCG, X-ray, magnetic resonance imaging, computerized tomography, single-photon emission computed tomography, positron emission tomography (PET), and fundus and ultrasound images have provided valuable information from various body parts for diagnosis, prognosis, and treatment. Biosensing, which integrates biology, chemistry, physics, information science, and technology, is an active branch in the field of science and technology. It has a broad application prospect in disease detection, environmental pollution monitoring, immune analysis, drug screening, and other areas.

However, there are many challenges that remain to be solved. The ability of a model to find statistical patterns across millions of samples and features is what enables superior performance for the intelligence system. However, most of the time, the identified patterns do not necessarily correspond to the underlying biologic pathways. Moreover, the numerical results driven by the machines, without a measure of their certainty and confidence level, do not provide trusted decisions. Finally, in practice, it is likely that a deployed medical decision-making system will encounter unseen disease conditions, where most of the existing system assumes that the universe of conditions is limited to what has been encoded in the models.

The objective of this Special Issue is to generate a comprehensive understanding of medical AI and biosensors in clinical applications. It will also highlight recent advances in the diverse implementations in healthcare management and monitoring. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in this field. The topics of interest are limited to the following:

  • Artificial intelligence for healthcare sensor data with applications;
  • Artificial intelligence for healthcare sensors and monitoring;
  • Interventional tracking and navigation;
  • Medical robotics and haptics;
  • Biosensors;
  • Biosensing technique;
  • Biochips;
  • Wearable devices;
  • Medical artificial intelligence;
  • Diagnosis artificial intelligence;
  • Predication medical artificial intelligence;
  • Medicine artificial intelligence ethical study;
  • Explainable medical artificial intelligence;
  • Medical imaging understanding;
  • Image segmentation, registration, and fusion;
  • Image reconstruction and image quality;
  • Computer-aided diagnosis;
  • Population imaging and imaging genetics;
  • Applications of big data in imaging;
  • Integration of imaging with non-imaging biomarkers;
  • Visualisation in biomedical Imaging;
  • Surgical data science;
  • Interventional imaging systems;
  • Image-guided interventions and surgery.

Dr. Zongyuan Ge
Dr. Donghao Zhang
Dr. Deval Mehta
Dr. Dwarikanath Mahapatra
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 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. Sensors 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.

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Published Papers (6 papers)

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Research

Jump to: Review

16 pages, 4713 KiB  
Article
Imaging Modalities in Inflammatory Breast Cancer (IBC) Diagnosis: A Computer-Aided Diagnosis System Using Bilateral Mammography Images
by Buket D. Barkana, Ahmed El-Sayed, Rana H. Khaled, Maha Helal, Hussein Khaled, Ruba Deeb, Mark Pitcher, Ruth Pfeiffer, Marilyn Roubidoux, Catherine Schairer and Amr S. Soliman
Sensors 2023, 23(1), 64; https://doi.org/10.3390/s23010064 - 21 Dec 2022
Cited by 5 | Viewed by 7470
Abstract
Inflammatory breast cancer (IBC) is an aggressive type of breast cancer. It leads to a significantly shorter survival than other types of breast cancer in the U.S. The American Joint Committee on Cancer (AJCC) defines the diagnosis based on specific criteria. However, the [...] Read more.
Inflammatory breast cancer (IBC) is an aggressive type of breast cancer. It leads to a significantly shorter survival than other types of breast cancer in the U.S. The American Joint Committee on Cancer (AJCC) defines the diagnosis based on specific criteria. However, the clinical presentation of IBC in North Africa (Egypt, Morocco, and Tunisia) does not agree, in many cases, with the AJCC criteria. Healthcare providers with expertise in IBC diagnosis are limited because of the rare nature of the disease. This paper reviewed current imaging modalities for IBC diagnosis and proposed a computer-aided diagnosis system using bilateral mammograms for early and improved diagnosis. The National Institute of Cancer in Egypt provided the image dataset consisting of IBC and non-IBC cancer cases. Type 1 and Type 2 fuzzy logic classifiers use the IBC markers that the expert team identified and extracted carefully. As this research is a pioneering work in its field, we focused on breast skin thickening, its percentage, the level of nipple retraction, bilateral breast density asymmetry, and the ratio of the breast density of both breasts in bilateral digital mammogram images. Granulomatous mastitis cases are not included in the dataset. The system’s performance is evaluated according to the accuracy, recall, precision, F1 score, and area under the curve. The system achieved accuracy in the range of 92.3–100%. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging)
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15 pages, 3888 KiB  
Article
Breast Tumor Tissue Image Classification Using DIU-Net
by Jiann-Shu Lee and Wen-Kai Wu
Sensors 2022, 22(24), 9838; https://doi.org/10.3390/s22249838 - 14 Dec 2022
Cited by 1 | Viewed by 1606
Abstract
Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer [...] Read more.
Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network, that is, to avoid the segmentation network from learning low-level features, we proposed the Complementary Color Conversion Scheme in the training phase. In addition, due to the disparity between the area of the nucleus and the background in the pathology image, there is an inherent data imbalance phenomenon, dice loss and focal loss were used to overcome this problem. In order to further strengthen the classification performance of the model, this study adopted a joint training scheme, so that the output of the classification network can not only be used to optimize the classification network itself, but also optimize the segmentation network. In addition, this model can also provide the pathologist model’s attention area, increasing the model’s interpretability. The classification performance verification of the proposed method was carried out with the BreaKHis dataset. Our method obtains binary/multi-class classification accuracy 97.24/93.75 and 98.19/94.43 for 200× and 400× images, outperforming existing methods. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging)
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19 pages, 2033 KiB  
Article
Local and Context-Attention Adaptive LCA-Net for Thyroid Nodule Segmentation in Ultrasound Images
by Zhen Tao, Hua Dang, Yueting Shi, Weijiang Wang, Xiaohua Wang and Shiwei Ren
Sensors 2022, 22(16), 5984; https://doi.org/10.3390/s22165984 - 10 Aug 2022
Cited by 15 | Viewed by 2617
Abstract
The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the [...] Read more.
The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with 7×1, 1×7 convolutions and the activation function Mish is designed, which enlarges the receptive field and extracts more feature details. Furthermore, a nodule adaptive convolution (NAC) module is introduced to adaptively deal with thyroid nodules of different sizes and positions, thereby improving the generalization performance of the model. Simultaneously, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. The proposed LCA-Net, validated on the public TN-SCUI2020 and TN3K datasets, achieves Dice scores of 90.26% and 82.08% and PA scores of 98.87% and 96.97%, respectively, which outperforms other state-of-the-art thyroid nodule segmentation models. This paper demonstrates the superiority of the proposed LCA-Net for thyroid nodule segmentation, which possesses strong generalization performance and promising segmentation accuracy. Consequently, the proposed model has wide application prospects for thyroid nodule diagnosis in clinics. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging)
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20 pages, 3998 KiB  
Article
BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images
by Ghada Atteia, Amel A. Alhussan and Nagwan Abdel Samee
Sensors 2022, 22(15), 5520; https://doi.org/10.3390/s22155520 - 24 Jul 2022
Cited by 41 | Viewed by 3885
Abstract
Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed [...] Read more.
Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging)
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12 pages, 5789 KiB  
Communication
PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation
by Jubao Han, Chao Xu, Ziheng An, Kai Qian, Wei Tan, Dou Wang and Qianqian Fang
Sensors 2022, 22(13), 4658; https://doi.org/10.3390/s22134658 - 21 Jun 2022
Cited by 1 | Viewed by 1948
Abstract
In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural network (parallel residual atrous pyramid network [...] Read more.
In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural network (parallel residual atrous pyramid network or PRAPNet) based on a parallel residual atrous pyramid module for the segmentation of intestinal polyp detection. We made full use of the global contextual information of the different regions by the proposed parallel residual atrous pyramid module. The experimental results showed that our proposed global prior module could effectively achieve better segmentation results in the intestinal polyp segmentation task compared with the previously published results. The mean intersection over union and dice coefficient of the model in the Kvasir-SEG dataset were 90.4% and 94.2%, respectively. The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, praNet, CaraNet, SFFormer-L, TransFuse-L). Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging)
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Review

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36 pages, 1290 KiB  
Review
Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review
by Joanna Czajkowska and Martyna Borak
Sensors 2022, 22(21), 8326; https://doi.org/10.3390/s22218326 - 30 Oct 2022
Cited by 8 | Viewed by 3080
Abstract
Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and [...] Read more.
Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Artificial Intelligence in Medical Imaging)
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