329 research outputs found
Computer aided diagnosis system for breast cancer using deep learning.
The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion modelsâ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumorâs background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumorsâ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmannâs machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
Automatic quality assessment in mammography screening:a deep learning based segmentation method
Abstract. Mammography is an imaging method used as a main tool to detect breast cancer at early stages. Images (mammograms) are examined by radiologists, who aim to identify cancerous findings. However, in order to do that, the mammograms need to be of diagnostic quality, which can sometimes be insufficient, and thus the quality of diagnosis also suffers.
Radiology technicians (radiographers) are trained to take mammography images, but not in every healthcare center a strict quality control process is established, which may substantially affect the patients. The most common defects in mammograms are positioning defects, which are seen in the images as skin-foldings or non-imaged parts of the breast.
The major issue at a process level is that the described positioning issues are noticed late, already at the diagnostic phase. If a radiologist decides that the mammogram is a non-diagnostic quality, the patient needs to revisit the imaging center. If quality control could be automated and standardized, unnecessary patient recalls could be avoided, thus, reducing the costs of the mammographic process. To date, there is a lack of automatic general quality control tools for mammography screening. Looking at the recent advances in artificial intelligence, it may be possible to automate this process.
The goal of this thesis was to develop an automatic system for quality assessment of mammograms. The author used Deep learning to develop an automatic framework for automatic segmentation of defects in mammograms using a dataset of 512 mammographic images extracted from the Oulu University Hospital archive. The second stage of the developed method performed quality assessment by analyzing the presence and location of different tissues in the images from the predicted segmentations.
The developed segmentation model yielded a Dice coefficient over 0.90 for the whole breast, breast, and pectoral muscle, and over 0.60 for skin-foldings and nipple.
The developed method is the first to tackle automatic segmentation of all major positioning issues in mammography. Ultimately, the developed technology has a potential to improve the mammography workflows and, eventually, patient outcomes.Automaattinen laadunarviointi mammografian kuvauksessa : syvÀoppimispohjainen segmentointimenetelmÀ. TiivistelmÀ. Mammografiaa on kuvantamismenetelmÀ, jota kÀytetÀÀn pÀÀvÀlineenÀ rintasyövÀn havaitsemiseksi varhaisessa vaiheessa. Radiologien on tutkittava mammogrammit ja pÀÀtettÀvÀ sitten, onko pahanlaatusia löydöksiÀ, ja tÀtÀ varten mammografiakuvien on oltava diagnostisesti laadukkaita.
Ammattilaiset koulutetaan mammografiakuvien ottamiseksi, mutta ei kaikissa terveyskeskuksissa on otettu kÀyttöön tiukka laadunvalvontaprosessi, joka voi vaikuttaa merkittÀvÀsti potilaisiin. Kuvissa voi olla virheitÀ, jotka tekevÀt kuvista ei-diagnostisen laadukkaan mammogrammin, ja ne voivat vaikuttaa diagnostiikkatuloksiin. Yksi nÀistÀ vioista ovat paikannusvirheet, joissa nÀkyvÀt kuvissa ihon taitoksina ja jotkut rinnan osat eivÀt nÀy.
Suurin ongelma prosessitasolla on, ettÀ kuvatut paikannusvirheet havaitaan myöhÀssÀ, jo diagnoosivaiheessa. Jos radiologit pÀÀttÀvÀt, ettÀ mammografiakuva ei ole diagnostisesti laadukas, potilaan on palattava kuvantamiskeskukseen ja tutkittava uudelleen, mikÀ voi lisÀtÀ kustannuksia ja työmÀÀrÀÀ. Jos laadunvalvonta voidaan automatisoida ja standardoida, voidaan vÀlttÀÀ tarpeetonta potilaan palauttamista ja vÀhentÀÀ siten mammografiaprosessin kustannuksia. TÀhÀn mennessÀ mammografiaseulonnassa ei ole automaattista yleistÀ laadunvalvontaa. Kun tarkastellaan tekoÀlyn viimeaikaisia edistystÀ, tÀmÀn prosessin automatisointi voi olla mahdollista.
TÀmÀn projektin tarkoituksena oli todistaa diagnostisten ja ei-diagnostisten laatumammogrammien automaattisen erottamisen toteutettavuus. Kirjoittaja kÀytti syvÀÀ oppimista automatisoidun kehyksen luomisessa kÀyttÀmÀllÀ 512 mammografiakuvaa, jotka otettiin Oulun yliopistollisen sairaalan arkistosta. Automaattisen menetelmÀn ensimmÀisessÀ vaiheessa suoritettiin rintakudosten ja ihon taittumien segmentointi. Toisessa vaiheessa suoritettiin laadunarviointi analysoimalla eri kudosten lÀsnÀolo ja sijainti kuvissa.
KehitetyllÀ segmentointimallilla saavutettiin merkittÀviÀ tuloksia, kun koko rinnan ja rintalihasten segmentoinnin onnistumisen hyvyttÀ mittaava Dice-kerroin oli yli 0,90, ja ihon taittumiselle ja nÀnnille yli 0,60.
Kehitetty menetelmÀ on ensimmÀinen, joka kÀsittelee mammografian kaikkien tÀrkeimpien paikannusvirheiden automaattista segmentointia. SillÀ on potentiaalia myötÀvaikuttaa mammografian työnkulkujen ja potilastulosten parantamiseen
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Mammography remains the most prevalent imaging tool for early breast cancer
screening. The language used to describe abnormalities in mammographic reports
is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a
correct BI-RADS category to each examined mammogram is a strenuous and
challenging task for even experts. This paper proposes a new and effective
computer-aided diagnosis (CAD) system to classify mammographic masses into four
assessment categories in BI-RADS. The mass regions are first enhanced by means
of histogram equalization and then semiautomatically segmented based on the
region growing technique. A total of 130 handcrafted BI-RADS features are then
extrcated from the shape, margin, and density of each mass, together with the
mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a
modified feature selection method based on the genetic algorithm (GA) is
proposed to select the most clinically significant BI-RADS features. Finally, a
back-propagation neural network (BPN) is employed for classification, and its
accuracy is used as the fitness in GA. A set of 500 mammogram images from the
digital database of screening mammography (DDSM) is used for evaluation. Our
system achieves classification accuracy, positive predictive value, negative
predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%,
and 79.3%, respectively. To our best knowledge, this is the best current result
for BI-RADS classification of breast masses in mammography, which makes the
proposed system promising to support radiologists for deciding proper patient
management based on the automatically assigned BI-RADS categories
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
- âŠ