Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.2
Journals
J. Imaging
Special Issues
Advances in IoMT, Deep Learning and Computer Vision for Mammographic...
share announcement

Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 40725

Special Issue Editors

Dr. Alessandro Bruno

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
Interests: computer vision; artificial intelligence; deep learning; image analysis and processing; visual saliency; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Pradeep N

E-Mail Website
Guest Editor
Department of Computer Science & Engineering, Bapuji Institute of Engineering and Technology, Davangere 577004, Karnataka, India
Interests: machine learning; medical image analysis; knowledge discovery; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Mangesh M. Ghonge

E-Mail Website
Guest Editor
Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik 422213, India
Interests: security in wireless networks; artificial intelligence; blockchain technology; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Elhoseny

E-Mail Website
Guest Editor
Department of Computer Science, College of Computer Information Technology, American University in the Emirates, Dubai 503000, United Arab Emirates
Interests: intelligent systems; data security; networks; Internet of Things (IoT); big data analysis; machine learning algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Faraz Janan

E-Mail Website
Guest Editor
1. Department of Computing, Imperial College London, London SW7 2RH, UK
2. School of Computing and Information Sciences, Anglia Ruskin University, Cambridge CB1 1PT, UK
Interests: image processing; medical image analysis; artificial intelligence
Dr. Gagandeep Jagdev

E-Mail Website
Guest Editor
Department of Computer Science, Punjabi University Guru Kashi College, Damdama Sahib, Punjab 151302, India
Interests: big data; cloud computing; data analytics; deep learning; diabetic retinopathy; digital image processing; mammography; intrusion detection system; machine learning

Special Issue Information

Dear Colleagues,

An increased demand for accurate biomedical data analysis tools has recently encouraged both the scientific community and industry to develop increasingly sophisticated techniques for clinical data analysis. This Special Issue focuses on three main topics: computer vision (CV), Artificial Intelligence (AI), and the Internet of Medical Things (IoMT), with applications to mammographic image analysis.

Breast screening techniques such as mammography, MRI, CT, and digital breast tomosynthesis (DBT) represent the most widely adopted clinical exams worldwide for detecting masses, microcalcifications, lesions, spiculated regions, and other types of suspicious regions. However, many radiologists are used to examining several images daily, and it can also be particularly challenging to detect cancers in dense breasts (BI-RADS D).

Radiologists and practitioners also provide textual reports describing the severity of tumours, if any. CV and natural language processing (NLP) techniques build upon DL, allowing stratification of the information pipeline extracted from images and medical reports. NLP systems could play a vital role in building a semantic knowledge base from unstructured text and encoding clinical information from mammograms and MRI reports.

Hardware and software applications are currently emerging to make versatile healthcare devices with cloud computing to effectively utilise computational resources [1,2]. IoMT models such as electrocardiogram architecture for patient monitoring and smartphone applications have been proposed for e-healthcare systems. Breast density classification based on IoMT represents a recent trend development fostering the integration of AI and IoMT techniques.

This issue will invite reviews, original empirical investigations, manuscripts showcasing innovative ideas, methods and applications involving computer vision and machine learning (classical, biological, and hybrid) methods to varying facets of mammographic image analysis. In addition, the Special Issue aims to feature papers on the recently evolving fields of “radiomics”, IoMT, CV, NLP, and DL—where contemporary computing methods are applied to identify radiomics biomarkers and extract “semantic” features for the digital decoding and understanding of medical images.

Scientific References

[1] Darwish, Ashraf and Hassanien, Aboul Ella and Elhoseny, Mohamed and Sangaiah, Arun Kumar and Muhammad, Khan. The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems, Journal of Ambient Intelligence and Humanized Computing, vol.10, number 10, pages 4151--4166, (2019), Springer

[2] Aceto, Giuseppe and Persico, Valerio and Pescapé, Antonio. Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0, Journal of Industrial Information Integration, vol. 18, pages 100129, (2020), Elseiver

Dr. Alessandro Bruno
Dr. Pradeep N
Dr. Mangesh M. Ghonge
Dr. Mohamed Elhoseny
Dr. Faraz Janan
Dr. Gagandeep Jagdev
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • mammogram analysis
  • computer vision
  • image analysis
  • segmentation
  • classification
  • unsupervised and supervised deep learning architectures
  • generative adversarial networks
  • variational autoencoders
  • natural language processing
  • data mining
  • IoMT (Internet of Medical Things)

Published Papers (11 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 1690 KiB  
Article
An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image
by Oleh Berezsky, Oleh Pitsun, Grygoriy Melnyk, Tamara Datsko, Ivan Izonin and Bohdan Derysh
J. Imaging 2023, 9(1), 12; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9010012 - 04 Jan 2023
Cited by 3 | Viewed by 1906
Abstract
The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, [...] Read more.
The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype “Luminal A”. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

15 pages, 5174 KiB  
Article
A Novel Approach of a Low-Cost UWB Microwave Imaging System with High Resolution Based on SAR and a New Fast Reconstruction Algorithm for Early-Stage Breast Cancer Detection
by Ibtisam Amdaouch, Mohamed Saban, Jaouad El Gueri, Mohamed Zied Chaari, Ana Vazquez Alejos, Juan Ruiz Alzola, Alfredo Rosado Muñoz and Otman Aghzout
J. Imaging 2022, 8(10), 264; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8100264 - 28 Sep 2022
Cited by 8 | Viewed by 2492
Abstract
In this article, a new efficient and robust approach—the high-resolution microwave imaging system—for early breast cancer diagnosis is presented. The core concept of the proposed approach is to employ a combination of a newly proposed delay-and-sum (DAS) algorithm and the specific absorption rate [...] Read more.
In this article, a new efficient and robust approach—the high-resolution microwave imaging system—for early breast cancer diagnosis is presented. The core concept of the proposed approach is to employ a combination of a newly proposed delay-and-sum (DAS) algorithm and the specific absorption rate (SAR) parameter to provide high image quality of breast tumors, along with fast image processing. The new algorithm enhances the tumor response by altering the parameter referring to the distance between the antenna and the tumor in the conventional DAS matrices. This adjustment entails a much clearer reconstructed image with short processing time. To achieve these aims, a high directional Vivaldi antenna is applied around a simulated hemispherical breast model with an embedded tumor. The detection of the tumor is carried out by calculating the maximum value of SAR inside the breast model. Consequently, the antenna position is relocated near the tumor region and is moved to nine positions in a trajectory path, leading to a shorter propagation distance in the image-creation process. At each position, the breast model is illuminated with short pulses of low power waves, and the back-scattered signals are recorded to produce a two-dimensional image of the scanned breast. Several simulations of testing scenarios for reconstruction imaging are investigated. These simulations involve different tumor sizes and materials. The influence of the number of antennas on the reconstructed images is also examined. Compared with the results from the conventional DAS, the proposed technique significantly improves the quality of the reconstructed images, and it detects and localizes the cancer inside the breast with high quality in a fast computing time, employing fewer antennas. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

19 pages, 2261 KiB  
Article
Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs
by Ana M. Mota, Matthew J. Clarkson, Pedro Almeida and Nuno Matela
J. Imaging 2022, 8(9), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8090231 - 29 Aug 2022
Cited by 4 | Viewed by 2175
Abstract
Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions [...] Read more.
Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

13 pages, 2839 KiB  
Article
Image Quality Comparison between Digital Breast Tomosynthesis Images and 2D Mammographic Images Using the CDMAM Test Object
by Ioannis A. Tsalafoutas, Angeliki C. Epistatou and Konstantinos K. Delibasis
J. Imaging 2022, 8(8), 223; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8080223 - 21 Aug 2022
Viewed by 1546
Abstract
To evaluate the image quality (IQ) of synthesized two-dimensional (s2D) and tomographic layer (TL) mammographic images in comparison to the 2D digital mammographic images produced with a new digital breast tomosynthesis (DBT) system. Methods: The CDMAM test object was used for IQ evaluation [...] Read more.
To evaluate the image quality (IQ) of synthesized two-dimensional (s2D) and tomographic layer (TL) mammographic images in comparison to the 2D digital mammographic images produced with a new digital breast tomosynthesis (DBT) system. Methods: The CDMAM test object was used for IQ evaluation of actual 2D images, s2D and TL images, acquired using all available acquisition modes. Evaluation was performed automatically using the commercial software that accompanied CDMAM. Results: The IQ scores of the TLs with the in-focus CDMAM were comparable, although usually inferior to those of 2D images acquired with the same acquisition mode, and better than the respective s2D images. The IQ results of TLs satisfied the EUREF limits applicable to 2D images, whereas for s2D images this was not the case. The use of high-dose mode (H-mode), instead of normal-dose mode (N-mode), increased the image quality of both TL and s2D images, especially when the standard mode (ST) was used. Although the high-resolution (HR) mode produced TL images of similar or better image quality compared to ST mode, HR s2D images were clearly inferior to ST s2D images. Conclusions: s2D images present inferior image quality compared to 2D and TL images. The HR mode produces TL images and s2D images with half the pixel size and requires a 25% increase in average glandular dose (AGD). Despite that, IQ evaluation results with CDMAM are in favor of HR resolution mode only for TL images and mainly for smaller-sized details. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

19 pages, 727 KiB  
Article
BI-RADS BERT and Using Section Segmentation to Understand Radiology Reports
by Grey Kuling, Belinda Curpen and Anne L. Martel
J. Imaging 2022, 8(5), 131; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8050131 - 09 May 2022
Cited by 8 | Viewed by 2924
Abstract
Radiology reports are one of the main forms of communication between radiologists and other clinicians, and contain important information for patient care. In order to use this information for research and automated patient care programs, it is necessary to convert the raw text [...] Read more.
Radiology reports are one of the main forms of communication between radiologists and other clinicians, and contain important information for patient care. In order to use this information for research and automated patient care programs, it is necessary to convert the raw text into structured data suitable for analysis. State-of-the-art natural language processing (NLP) domain-specific contextual word embeddings have been shown to achieve impressive accuracy for these tasks in medicine, but have yet to be utilized for section structure segmentation. In this work, we pre-trained a contextual embedding BERT model using breast radiology reports and developed a classifier that incorporated the embedding with auxiliary global textual features in order to perform section segmentation. This model achieved 98% accuracy in segregating free-text reports, sentence by sentence, into sections of information outlined in the Breast Imaging Reporting and Data System (BI-RADS) lexicon, which is a significant improvement over the classic BERT model without auxiliary information. We then evaluated whether using section segmentation improved the downstream extraction of clinically relevant information such as modality/procedure, previous cancer, menopausal status, purpose of exam, breast density, and breast MRI background parenchymal enhancement. Using the BERT model pre-trained on breast radiology reports, combined with section segmentation, resulted in an overall accuracy of 95.9% in the field extraction tasks. This is a 17% improvement, compared to an overall accuracy of 78.9% for field extraction with models using classic BERT embeddings and not using section segmentation. Our work shows the strength of using BERT in the analysis of radiology reports and the advantages of section segmentation by identifying the key features of patient factors recorded in breast radiology reports. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

17 pages, 6177 KiB  
Article
Elimination of Defects in Mammograms Caused by a Malfunction of the Device Matrix
by Dmitrii Tumakov, Zufar Kayumov, Alisher Zhumaniezov, Dmitry Chikrin and Diaz Galimyanov
J. Imaging 2022, 8(5), 128; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8050128 - 02 May 2022
Cited by 5 | Viewed by 2960
Abstract
Today, the processing and analysis of mammograms is quite an important field of medical image processing. Small defects in images can lead to false conclusions. This is especially true when the distortion occurs due to minor malfunctions in the equipment. In the present [...] Read more.
Today, the processing and analysis of mammograms is quite an important field of medical image processing. Small defects in images can lead to false conclusions. This is especially true when the distortion occurs due to minor malfunctions in the equipment. In the present work, an algorithm for eliminating a defect is proposed, which includes a change in intensity on a mammogram and deteriorations in the contrast of individual areas. The algorithm consists of three stages. The first is the defect identification stage. The second involves improvement and equalization of the contrasts of different parts of the image outside the defect. The third involves restoration of the defect area via a combination of interpolation and an artificial neural network. The mammogram obtained as a result of applying the algorithm shows significantly better image quality and does not contain distortions caused by changes in brightness of the pixels. The resulting images are evaluated using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Naturalness Image Quality Evaluator (NIQE) metrics. In total, 98 radiomics features are extracted from the original and obtained images, and conclusions are drawn about the minimum changes in features between the original image and the image obtained by the proposed algorithm. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

14 pages, 8913 KiB  
Article
YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings
by Alexey Kolchev, Dmitry Pasynkov, Ivan Egoshin, Ivan Kliouchkin, Olga Pasynkova and Dmitrii Tumakov
J. Imaging 2022, 8(4), 88; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8040088 - 24 Mar 2022
Cited by 10 | Viewed by 3344
Abstract
Background: We directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model. Method: We used 1080 images to train the YOLOv4, [...] Read more.
Background: We directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model. Method: We used 1080 images to train the YOLOv4, plus 100 images with proven breast cancer (BC) and 100 images with proven absence of BC to test both models. Results: the rates of true-positive, false-positive and false-negative outcomes were 60, 10 and 40, respectively, for YOLOv4, and 93, 63 and 7, respectively, for NCA. The sensitivities for the YOLOv4 and the NCA were comparable to each other for star-like lesions, masses with unclear borders, round- or oval-shaped masses with clear borders and partly visualized masses. On the contrary, the NCA was superior to the YOLOv4 in the case of asymmetric density and of changes invisible on the dense parenchyma background. Radiologists changed their earlier decisions in six cases per 100 for NCA. YOLOv4 outputs did not influence the radiologists’ decisions. Conclusions: in our set, NCA clinically significantly surpasses YOLOv4. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 615 KiB  
Review
AI in Breast Cancer Imaging: A Survey of Different Applications
by João Mendes, José Domingues, Helena Aidos, Nuno Garcia and Nuno Matela
J. Imaging 2022, 8(9), 228; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8090228 - 26 Aug 2022
Cited by 11 | Viewed by 3832
Abstract
Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. [...] Read more.
Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant—which could be important to diminish reading time and improve accuracy—are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

18 pages, 4972 KiB  
Review
Multiple Papillomas of the Breast: A Review of Current Evidence and Challenges
by Rossella Rella, Giovanna Romanucci, Damiano Arciuolo, Assunta Scaldaferri, Enida Bufi, Sebastiano Croce, Andrea Caulo and Oscar Tommasini
J. Imaging 2022, 8(7), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8070198 - 13 Jul 2022
Cited by 1 | Viewed by 4838
Abstract
Objectives: To conduct a review of evidence about papillomatosis/multiple papillomas (MP), its clinical and imaging presentation, the association between MP and malignancy and the management strategies that follow. Methods: A computerized literature search using PubMed and Google Scholar was performed up to January [...] Read more.
Objectives: To conduct a review of evidence about papillomatosis/multiple papillomas (MP), its clinical and imaging presentation, the association between MP and malignancy and the management strategies that follow. Methods: A computerized literature search using PubMed and Google Scholar was performed up to January 2021 with the following search strategy: “papilloma” OR “intraductal papilloma” OR “intraductal papillary neoplasms” OR “papillomatosis” OR “papillary lesion” AND “breast”. Two authors independently conducted a search, screening and extraction of data from the eligible studies. Results: Of the 1881 articles identified, 29 articles met the inclusion criteria. The most common breast imaging methods (mammography, ultrasound) showed few specific signs of MP, and evidence about magnetic resonance imaging were weak. Regarding the association between MP and malignancy, the risk of underestimation to biopsy methods and the frequent coexistence of MP and other high-risk lesions needs to be taken into consideration. Results about the risk of developing breast carcinoma of patients affected by MP were inconsistent. Conclusions: MP is a challenge for all breast specialists, and familiarity with its features is required to make the correct diagnosis. Further studies are needed to evaluate the factors to take into account to plan management, time of follow-up and imaging methods. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

22 pages, 28214 KiB  
Review
Image Augmentation Techniques for Mammogram Analysis
by Parita Oza, Paawan Sharma, Samir Patel, Festus Adedoyin and Alessandro Bruno
J. Imaging 2022, 8(5), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8050141 - 20 May 2022
Cited by 41 | Viewed by 5759
Abstract
Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming [...] Read more.
Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

40 pages, 1107 KiB  
Review
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms
by Parita Oza, Paawan Sharma, Samir Patel and Alessandro Bruno
J. Imaging 2021, 7(9), 190; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7090190 - 18 Sep 2021
Cited by 32 | Viewed by 6033
Abstract
Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used [...] Read more.
Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic. Full article
(This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-11
Back to TopTop