scholarly journals The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1055
Author(s):  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Jun Oyama ◽  
Emi Yamaga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Clinical Practice ◽  
Data Extraction ◽  
Ultrasonic Imaging ◽  
Image Synthesis ◽  
Breast Ultrasound ◽  
Learning Technology ◽  
Patient Prognosis ◽  
Improve Patient

Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

scholarly journals Prolonged delirium misdiagnosed as a mood disorder

2017 ◽  
Vol 11 (2) ◽  
pp. 206-208
Author(s):  
Fei Cao ◽  
Haitham Salem ◽  
Caesa Nagpal ◽  
Antonio L. Teixeira
Keyword(s):  
Clinical Practice ◽  
Mood Disorder ◽  
Clinical Status ◽  
Hospital Setting ◽  
Cognitive Symptoms ◽  
Precipitating Factors ◽  
Multiple Factors ◽  
Symptomatic Management ◽  
Patient Prognosis ◽  
Improve Patient

ABSTRACT Delirium can be conceptualized as an acute decline in cognitive function that typically lasts from hours to a few days. Prolonged delirium can also affect patients with multiple predisposing and/or precipitating factors. In clinical practice, prolonged delirium is often unrecognized, and can be misdiagnosed as other psychiatric disorders. We describe a case of a 59-year-old male presenting with behavioral and cognitive symptoms that was first misdiagnosed as a mood disorder in a general hospital setting. After prolonged delirium due to multiple factors was confirmed, the patient was treated accordingly with symptomatic management. He evolved with progressive improvement of his clinical status. Early diagnosis and management of prolonged delirium are important to improve patient prognosis and avoid iatrogenic measures.

scholarly journals Lesion Segmentation in Breast Ultrasound Images using Optimized Marked Watershed Method

2021 ◽  
Author(s):  
Xiaoyan Shen ◽  
He Ma ◽  
Ruibo Liu ◽  
Hong Li ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Stage ◽  
Speckle Noise ◽  
Histogram Equalization ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Early Screening ◽  
Side Window

Abstract Background: Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumors in early stage. However, due to the lack of radiologists with professional skills, ultrasound based breast cancer screening has not been widely used in rural area. Computer-aided diagnosis (CAD) technology can effectively alleviates this problem. Since Breast Ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD system, is challenging.Results: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 BUS images from open source. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model, RDAU–NET. And its’ Accuracy(Acc), Dice efficient(DSC) and Jaccard Index(JI) reached 96.25%, 78.4% and 65.34% on dataset A, and ACC, DC and Sen reached 97.96%, 86.25% and 88.79% on dataset B.Conclusions: We proposed an adaptive morphology snake based on marked watershed(AMSMW) algorithm for BUS images segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. What’s more, since the Rectangular Region of Interest(RROI) in the proposed method is drawn manually, we will consider adding deep learning network to automatically identify RROI, and completely liberate the hands of radiologists.Methods: The proposed method consists of two main steps. In the first step, we used Contrast Limited Adaptive Histogram Equalization(CLAHE) and Side Window Filter(SWF) to preprocess BUS images. Therefore, lesion contours can be effectively highlighted and the influence of noise can be eliminated to a great extent. In the second step, we proposed adaptative morphology snake(AMS) as an embedded segmentation function of AMSMW. It can adjust working parameters adaptively, according to different lesions’ size. By combining segmentation results of AMS with marker region obtained by morphological method, we got the marker region of marked watershed (MW). Finally, we obtained candidate contours by MW. And the best lesion contour was chosen by maximum Average Radial Derivative(ARD).

scholarly journals Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianyu Zhang ◽  
Hui Li ◽  
Chaoyun Wang ◽  
Wen Cheng ◽  
Yuntao Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Molecular Subtypes ◽  
Learning Model ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Test Set ◽  
External Test ◽  
Deep Learning Model

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

Deep learning algorithm performance in mammography screening: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13553-e13553
Author(s):  
Rosimeire Aparecida Roela ◽  
Gabriel Vansuita Valente ◽  
Carlos Shimizu ◽  
Rossana Veronica Mendoza Lopez ◽  
Tatiana Cardoso de Mello Tucunduva ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Mammography Screening ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Meta Analysis ◽  
Paper Analysis

e13553 Background: Mammography interpretation presents some challenges however, better technological approaches have allowed increased accuracy in cancer diagnosis and nowadays, radiologists sensitivity and specificity for mammography screening vary from 84.5 to 90.6 and 89.7 to 92.0%, respectively. Since its introduction in breast image analysis, artificial intelligence (AI) has rapidly improved and deep learning methods are gaining relevance as a companion tool to radiologists. Thus, the aim of this systematic review and meta analysis was to evaluate the sensitivity and specificity of AI deep learning algorithms and radiologists for breast cancer detection through mammography. Methods: A systematic review was performed using PubMed and the words: deep learning or convolutional neural network and mammography or mammogram, from January 2015 to October 2020. All titles and abstracts were doubly checked; duplicate studies and studies in languages other than English were excluded. The remaining complete studies were doubly assessed and those with specificity and sensibility information had data collected. For the meta analysis, studies reporting specificity, sensitivity and confidence intervals were selected. Heterogeneity measures were calculated using Cochran Q test (chi-square test) and the I2 (percentage of variation). Sensitivity and specificity and 95% confidence intervals (CI) values were calculated, using Stata/MP 14.0 for Windows. Results: Among 223 studies, 66 were selected for full paper analysis and 24 were selected for data extraction. Subsequently, only papers evaluating sensitivity, especificity, CI and/or AUC were analyzed. Eleven studies compared AUC using AI with another method and for these studies, a differential AUC was calculated, however no differences were observed: AI vs Reader (n = 3; p = 0.109); AI vs AI (n = 5; p = 0.225); AI vs AI + reader (n = 2; p = 0.180); AI + Reader vs reader (n = 2; p = 0.655); AI vs reader (n > 1) (n = 3; p = 0.102). Some studies had more than one comparison. A meta analysis was performed to evaluate sensitivity and specificity of the methods. Five studies were included in this analysis and a great heterogeneity among them was observed. There were studies evaluating more than one AI algorithm and studies comparing AI with readers alone or in combination with AI. Sensitivity for AI; AI + reader; reader alone, were 76.08; 84.02; 80.91, respectively. Specificity for AI; AI + reader; reader alone, were 96.62; 85.67; 84.89, respectively. Results are shown in the table. Conclusions: Although recent improvements in AI algorithms for breast cancer screening, a delta AUC between comparisons of AI algorithms and readers was not observed.[Table: see text]

scholarly journals Preliminary study of the technical limitations of automated breast ultrasound: from procedure to diagnosis

2020 ◽  
Vol 53 (5) ◽  
pp. 293-300 ◽  
Author(s):  
Maria Julia Gregório Calas ◽  
Fernanda Philadelpho Arantes Pereira ◽  
Leticia Pereira Gonçalves ◽  
Flávia Paiva Proença Lobo Lopes
Keyword(s):  
Breast Cancer ◽  
Clinical Practice ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Breast Ultrasound ◽  
Routine Clinical Practice ◽  
National Context ◽  
Conventional Ultrasound ◽  
Preliminary Study ◽  
Automated Breast Ultrasound

Abstract Objective: To evaluate the main technical limitations of automated breast ultrasound and to determine the proportion of examinations excluded. Materials and Methods: We evaluated 440 automated breast ultrasound examinations performed, over a 12-month period, by technicians using an established protocol. Results: In five cases (1.1%), the examination was deemed unacceptable for diagnostic purposes, those examinations therefore being excluded. Conclusion: Automated breast ultrasound is expected to overcome some of the major limitations of conventional ultrasound in breast cancer screening. In Brazil, this new method can be accepted for inclusion in routine clinical practice only after its advantages have been validated in the national context.

scholarly journals Key Algorithms for Segmentation of Copperplate Printing Image Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ye Zhang ◽  
Qiu Xie ◽  
Canlin Zhang
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Fuzzy Set ◽  
Image Synthesis ◽  
Original Data ◽  
Synthesis Process ◽  
Learning Technology ◽  
The Neural Network ◽  
Functional Learning ◽  
Main Color

As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short.

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