scholarly journals Development of Gastric Lesion Detection Algorithm based on AI

2021 ◽  
Vol 10 (6) ◽  
pp. 655-664
Author(s):  
Jae-Seoung Kim ◽  
Dong Kyun Park
Keyword(s):  
Gastric Lesion ◽  
Detection Algorithm ◽  
Lesion Detection

scholarly journals Automated diabetic retinopathy grading and lesion detection based on the modified R‐FCN object‐detection algorithm

2020 ◽  
Vol 14 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Jialiang Wang ◽  
Jianxu Luo ◽  
Bin Liu ◽  
Rui Feng ◽  
Lina Lu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Object Detection ◽  
Detection Algorithm ◽  
Lesion Detection

scholarly journals DETECTING PROTRUSION LESION IN DIGESTIVE TRACT USING A SINGLE-STAGE DETECTION METHOD

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 231-235
Author(s):  
L. Wang ◽  
S. Wang ◽  
S. Huang ◽  
C. Liu
Keyword(s):  
Digestive Tract ◽  
Detection Method ◽  
Detection Algorithm ◽  
Lesion Detection ◽  
Single Stage ◽  
Great Success ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
Multi Scale ◽  
Long Time

<p><strong>Abstract.</strong> The classification networks have already existed for a long time and achieve great success. However, in biomedical image processing, classifying normal and abnormal ones only is not enough clinically, the desired output should include localization, i.e., where the lesion is. In this paper, we present a method for detecting protrusion lesion in digestive tract. We use a deep learning-based model to build a computer-aided diagnosis system to help doctors examine the intestinal diseases. Learn from existing detection method, one-stage and two-stage detection algorithm, a new network suitable for protrusion lesion detection is proposed. We inherit the method of anchor generation in SSD, a fast single-stage object detector outperform R-CNN series in terms of speed. Multi-scale feature layers are assigned to generate different sizes of default anchor boxes. Different from the previous work, our method doesnt require additional preprocessing because the network can learn features autonomously. For the 256*256 input, our method achieves 73% AP, perform a novel way to detect protrusion lesions.</p>

scholarly journals Development and Validation of Suspicious Liver Lesion Deep Learning Algorithm for Computed Tomography

2021 ◽  
Author(s):  
Jacob Johnson ◽  
Kaneel Senevirathne ◽  
Lawrence Ngo
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Small Sample Size ◽  
Liver Lesion ◽  
The United States ◽  
Detection Algorithm ◽  
Small Sample ◽  
Lesion Detection ◽  
Liver Lesions ◽  
Deep Learning Algorithm

In this work, we report the results of a deep-learning based liver lesion detection algorithm. While several liver lesion segmentation and classification algorithms have been developed, none of the previous work has focused on detecting suspicious liver lesions. Furthermore, their generalizability remains a pitfall due to their small sample size and sample homogeneity. Here, we developed and validated a highly generalizable deep-learning algorithm for detection of suspicious liver lesions. The algorithm was trained and tested on a diverse dataset containing CT exams from over 2,000 hospital sites in the United States. Our final model achieved an AUROC of 0.84 with a specificity of 0.99 while maintaining a sensitivity of 0.33.

EVALUATION OF STELLATE LESION DETECTION IN A STANDARD MAMMOGRAM DATA SET

1993 ◽  
Vol 07 (06) ◽  
pp. 1477-1492 ◽  
Author(s):  
W. PHILIP KEGELMEYER
Keyword(s):  
Image Data ◽  
Detection Algorithm ◽  
Lesion Detection ◽  
Final Report ◽  
Integration Step ◽  
Spatial Integration ◽  
Data Set ◽  
Performance Statistics ◽  
Performance Results ◽  
Is Value

We have previously reported on a method for the automatic detection of stellate lesions in digitized mammograms, and on our tests of that method on image data with known diagnoses. This earlier investigation was based on a limited set of 10 test images, each with a stellate lesion. As our approach is one of supervised training, half of the data was used as a training set, and so the performance results were necessarily coarse. Accordingly there is value in testing these algorithms on a larger data set that will not only provide more lesions but also truly undiseased tissue. A new mammogram data set addresses both of these concerns, as it contains examples of twelve stellate lesions, as well as fifty examples of entirely normal mammograms. Further, as this data set has been made widely available to all interested researchers, performance results for specific algorithms on this data set are of particular value, as they can be directly compared to the performance of other algorithms similarly applied. Thus the main contribution of the current paper is to exhaustively evaluate the performance of this stellate lesion detection algorithm on the new mammogram data set. A secondary aim is to present a revision of the spatial integration step which generates the final report of a lesion’s existence, one that facilitates the extraction of ROC performance statistics.

scholarly journals Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Jean Marie Vianney Kinani ◽  
Alberto Jorge Rosales Silva ◽  
Francisco Gallegos Funes ◽  
Dante Mújica Vargas ◽  
Eduardo Ramos Díaz ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Brain Lesion ◽  
Fuzzy Rule ◽  
Computation Time ◽  
Detection Algorithm ◽  
Lesion Detection ◽  
Fluid Attenuated Inversion Recovery ◽  
Level Set Evolution

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

A massive lesion detection algorithm in mammography

2005 ◽  
Vol 21 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Francesco Fauci ◽  
Giuseppe Raso ◽  
Rosario Magro ◽  
Giustina Forni ◽  
Adele Lauria ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Lesion Detection

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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