3,652 research outputs found
Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques
Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations
Combining machine learning and deep learning approaches to detect cervical cancer in cytology images
This dissertation is centred around the implementation and optimization of a hybrid pipeline for the identification and stratification of abnormal cell regions in cytology images, combining state of the art deep learning (DL) approaches and conventional machine learning (ML) models.Cervical cancer is the fourth most common cancer in women. When diagnosed early on, it is one of the most successfully treatable types of cancer. As such, screening tests are very effective as a prevention measure. These tests involve the analysis of microscopic fields of cytology samples which, when performed manually, is a very demanding task, requiring highly specialized laboratory technologists (cytotechs). Due to this, there has been a great interest in automating the overall screening process. Most of these computer-aided diagnosis systems subject the images from each sample to a set of steps, more notably focus and adequacy assessment, region of interest identification and respective classification. This work is focused on the last two stages, more specifically, the detection of abnormal regions and the classification of their abnormality level. The main approaches can be divided into two types: deep learning architectures and conventional machine learning models, both presenting their own set of advantages and disadvantages.
This work explores the combination of both of these approaches in hybrid pipelines to minimize the problems of each one whilst taking advantage of the best they have to offer, ultimately contributing to a decision support system for cervical cancer diagnosis. More specifically, it is proposed a deep-learning approach for the detection of the regions of interest and respective bounding-box generation, followed by a simpler machine-learning model for their classification. Furthermore, a comparative analysis of different hybrid pipelines and algorithms will also be performed, aiming to support future research of similar solutions
Quality Assurance of Cervical Smear Slide Inspection Using a Novel Eye-Tracking Technique
A novel objective quality assurance system for smear slide screening is
investigated in this thesis. A method of data validation was developed that
compares data from an eye tracked image display, machine image colour texture
analysis and expert judgements in a statistical manner to identify salient areas of
cervical cytological images. These data are used to construct screener
performance profiles, which have been compared to screener experience. The
experimental methodology is described and how the screener performance profile
is constructed. Results from a study of 10 screeners, checkers and pathologists
are presented showing predicted trends of human performance. Relations to
experience and strategy are also shown, though these relationships are not
statistically significant. A standardised quality assurance test is developed that
profiles screeners across many performance measures. Highly significant
correlations were found between fixation saliency and machine colour texture
(maxima density), though fixation saliency suffers from a lack of a significant
statistical basis. Further fixation data is needed, however if it conforms to the
existing trends then the results would support the new data validation method as a
framework from which image analysis techniques applied to cytology may be
objectively tested. Furthermore, this new approach to cervical cytology quality
assurance would have the potential to further reduce human errors in the cervical
smear inspection process by lowering levels of observer variation found in all
aspects of the cervical screening process
Data fusion techniques for biomedical informatics and clinical decision support
Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv
Labelling imaging datasets on the basis of neuroradiology reports: a validation study
Natural language processing (NLP) shows promise as a means to automate the
labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI)
datasets for computer vision applications. To date, however, there has been no
thorough investigation into the validity of this approach, including
determining the accuracy of report labels compared to image labels as well as
examining the performance of non-specialist labellers. In this work, we draw on
the experience of a team of neuroradiologists who labelled over 5000 MRI
neuroradiology reports as part of a project to build a dedicated deep
learning-based neuroradiology report classifier. We show that, in our
experience, assigning binary labels (i.e. normal vs abnormal) to images from
reports alone is highly accurate. In contrast to the binary labels, however,
the accuracy of more granular labelling is dependent on the category, and we
highlight reasons for this discrepancy. We also show that downstream model
performance is reduced when labelling of training reports is performed by a
non-specialist. To allow other researchers to accelerate their research, we
make our refined abnormality definitions and labelling rules available, as well
as our easy-to-use radiology report labelling app which helps streamline this
process
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