scholarly journals The Establishment of Transformer-Based Computer-Aided Diagnosis Model to Improve the Classification Consistency of BI-RADS-US3–5 Nodules Among Radiologists: A Multi-Center Study

2021 ◽  
Author(s):  
Hongtao Ji ◽  
Qiang Zhu ◽  
Teng Ma ◽  
Yun Cheng ◽  
Shuai Zhou ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Cad Model ◽  
Data Systems ◽  
Test Set ◽  
Classification Consistency ◽  
Computer Aided ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Abstract Background: Significant differences exist in classification outcomes for radiologists using ultrasonography-based breast imaging-reporting and data systems for diagnosing category 3–5 (BI-RADS-US 3–5) breast nodules, due to a lack of clear and distinguishing image features. As such, this study investigates the use of a transformer-based computer-aided diagnosis (CAD) model for improved BI-RADS-US 3–5 classification consistency.Methods: Five radiologists independently performed BI-RADS-US annotations on a breast ultrasonography image set collected from 20 hospitals in China. The data were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity, specificity, and accuracy were calculated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS-US classification results for the sampling test set, provided by CAD, to determine whether classification consistency (the kappa value)，sensitivity, specificity, and accuracy had improved.Results: Classification accuracy for the CAD model applied to the test set was 95.7% for category 3 nodules, 97.6% for category 4A nodules, 95.60% for category 4B nodules, 94.2% for category 4C nodules, and 97.5% for category 5 nodules. Adjustments were made to 1,583 nodules, as 905 were classified to a higher category and 678 to a lower category in the sampling test set. As a result, the accuracy, sensitivity, and specificity of classification by each radiologist improved, with the consistency (kappa values) for all radiologists increasing to >0.60.Conclusions: The proposed transformer-based CAD model improved BI-RADS-US 3–5 nodule classification by individual radiologists and increased diagnostic consistency.

Deep Learning for Computer-aided Diagnosis of Pneumoconiosis

2021 ◽  
Author(s):  
Zheng Wang ◽  
Qingjun Qian ◽  
Jianfang Zhang ◽  
Caihong Duo ◽  
Wen He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Diagnostic Sensitivity ◽  
Junior Doctors ◽  
Diagnostic Efficiency ◽  
Test Set ◽  
Computer Aided ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Abstract Background: The diagnosis of pneumoconiosis relies primarily on chest radiographs and exhibits significant variability between physicians. Computer-aided diagnosis (CAD) can improve the accuracy and consistency of these diagnoses. However, CAD based on machine learning requires extensive human intervention and time-consuming training. As such, deep learning has become a popular tool for the development of CAD models. In this study, the clinical applicability of CAD based on deep learning was verified for pneumoconiosis patients.Methods: Chest radiographs were collected from 5424 occupational health examiners who met the inclusion criteria. The data were divided into training, validation, and test sets. The CAD algorithm was then trained and applied to processing of the validation set, while the test set was used to evaluate diagnostic efficacy. Three junior and three senior physicians provided independent diagnoses using images from the test set and a comprehensive diagnosis for comparison with the CAD results. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the proposed CAD system. A McNemar test was used to evaluate diagnostic sensitivity and specificity for pneumoconiosis, both before and after the use of CAD. A kappa consistency test was used to evaluate the diagnostic consistency for both the algorithm and the clinicians.Results: ROC results suggested the proposed CAD model achieved high accuracy in the diagnosis of pneumoconiosis, with a kappa value of 0.90. The sensitivity, specificity, and kappa values for the junior doctors increased from 0.86 to 0.98, 0.68 to 0.86, and 0.54 to 0.84, respectively (p<0.05), when CAD was applied. However, metrics for the senior doctors were not significantly different.Conclusion: DL-based CAD can improve the diagnostic sensitivity, specificity, and consistency of pneumoconiosis diagnoses, particularly for junior physicians.

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

scholarly journals Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
D. K. Iakovidis ◽  
T. Goudas ◽  
C. Smailis ◽  
I. Maglogiannis
Keyword(s):  
Kidney Biopsy ◽  
Image Annotation ◽  
Image Force ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Obstructive Nephropathy ◽  
Common Disease ◽  
Annotation Tool ◽  
Computer Aided ◽  
Aided Diagnosis

Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

scholarly journals Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

2011 ◽  
pp. 153-158
Author(s):  
Aswini Kumar Mohanty ◽  
Saroj Kumar Lenka
Keyword(s):  
Data Mining ◽  
Diagnostic Criteria ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Data Mining Technique ◽  
Cad Systems ◽  
Computer Aided ◽  
Using Data ◽  
Aided Diagnosis

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases

2012 ◽  
pp. 86-106
Author(s):  
Issam El Naqa ◽  
Jung Hun Oh ◽  
Yongyi Yang
Keyword(s):  
Relevance Feedback ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Query Process ◽  
Large Databases ◽  
Computer Aided ◽  
High Level ◽  
Aided Diagnosis

With the ever-growing volume of images used in medicine, the capability to retrieve relevant images from large databases is becoming increasingly important. Despite the recent progress made in the field, its applications in Computer-Aided Diagnosis (CAD) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features. Relevance Feedback (RFB) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images, which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy. In this chapter, the authors review some recent advances in RFB technology, and discuss its expanding role in content-based image retrieval from medical archives. They provide working examples, based on their experience, for developing machine-learning methods for RFB in mammography and highlight the potential opportunities in this field for CAD applications and clinical decision-making.

scholarly journals A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Said Boumaraf ◽  
Xiabi Liu ◽  
Chokri Ferkous ◽  
Xiaohong Ma
Keyword(s):  
Feature Selection ◽  
Predictive Value ◽  
Breast Imaging ◽  
Feature Selection Method ◽  
Computer Aided Diagnosis ◽  
Screening Mammography ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

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 extracted 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 for 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.

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