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When the machine does not know measuring uncertainty in deep learning models of medical images
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecently, Deep learning (DL), which involves powerful black box predictors, has outperformed
human experts in several medical diagnostic problems. However, these methods focus
exclusively on improving the accuracy of point predictions without assessing their outputs’
quality and ignore the asymmetric cost involved in different types of misclassification errors.
Neural networks also do not deliver confidence in predictions and suffer from over and
under confidence, i.e. are not well calibrated. Knowing how much confidence there is in a
prediction is essential for gaining clinicians’ trust in the technology.
Calibrated uncertainty quantification is a challenging problem as no ground truth is
available. To address this, we make two observations: (i) cost-sensitive deep neural networks
with Dropweights models better quantify calibrated predictive uncertainty, and (ii) estimated
uncertainty with point predictions in Deep Ensembles Bayesian Neural Networks with
DropWeights can lead to a more informed decision and improve prediction quality.
This dissertation focuses on quantifying uncertainty using concepts from cost-sensitive
neural networks, calibration of confidence, and Dropweights ensemble method. First, we
show how to improve predictive uncertainty by deep ensembles of neural networks with Dropweights
learning an approximate distribution over its weights in medical image segmentation
and its application in active learning. Second, we use the Jackknife resampling technique
to correct bias in quantified uncertainty in image classification and propose metrics to measure
uncertainty performance. The third part of the thesis is motivated by the discrepancy
between the model predictive error and the objective in quantified uncertainty when costs for
misclassification errors or unbalanced datasets are asymmetric. We develop cost-sensitive
modifications of the neural networks in disease detection and propose metrics to measure the
quality of quantified uncertainty. Finally, we leverage an adaptive binning strategy to measure
uncertainty calibration error that directly corresponds to estimated uncertainty performance
and address problematic evaluation methods.
We evaluate the effectiveness of the tools on nuclei images segmentation, multi-class
Brain MRI image classification, multi-level cell type-specific protein expression prediction in
ImmunoHistoChemistry (IHC) images and cost-sensitive classification for Covid-19 detection
from X-Rays and CT image dataset. Our approach is thoroughly validated by measuring the
quality of uncertainty. It produces an equally good or better result and paves the way for the
future that addresses the practical problems at the intersection of deep learning and Bayesian
decision theory.
In conclusion, our study highlights the opportunities and challenges of the application of
estimated uncertainty in deep learning models of medical images, representing the confidence of the model’s prediction, and the uncertainty quality metrics show a significant improvement
when using Deep Ensembles Bayesian Neural Networks with DropWeights
Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
A model's ability to express its own predictive uncertainty is an essential
attribute for maintaining clinical user confidence as computational biomarkers
are deployed into real-world medical settings. In the domain of cancer digital
histopathology, we describe a novel, clinically-oriented approach to
uncertainty quantification (UQ) for whole-slide images, estimating uncertainty
using dropout and calculating thresholds on training data to establish cutoffs
for low- and high-confidence predictions. We train models to identify lung
adenocarcinoma vs. squamous cell carcinoma and show that high-confidence
predictions outperform predictions without UQ, in both cross-validation and
testing on two large external datasets spanning multiple institutions. Our
testing strategy closely approximates real-world application, with predictions
generated on unsupervised, unannotated slides using predetermined thresholds.
Furthermore, we show that UQ thresholding remains reliable in the setting of
domain shift, with accurate high-confidence predictions of adenocarcinoma vs.
squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts
A Review of Atrial Fibrillation Detection Methods as a Service
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals
Automatic construction of rule-based ICD-9-CM coding systems
Background: In this paper we focus on the problem of automatically constructing ICD-9-CM coding systems for radiology reports. ICD-9-CM codes are used for billing purposes by health institutes and are assigned to clinical records manually following clinical treatment. Since this labeling task requires expert knowledge in the field of medicine, the process itself is costly and is prone to errors as human annotators have to consider thousands of possible codes when assigning the right ICD-9-CM labels to a document. In this study we use the datasets made available for training and testing automated ICD-9-CM coding systems by the organisers of an International Challenge on Classifying Clinical Free Text Using Natural Language Processing in spring 2007. The challenge itself was dominated by entirely or partly rule-based systems that solve the coding task using a set of hand crafted expert rules. Since the feasibility of the construction of such systems for thousands of ICD codes is indeed questionable, we decided to examine the problem of automatically constructing similar rule sets that turned out to achieve a remarkable accuracy in the shared task challenge. Results: Our results are very promising in the sense that we managed to achieve comparable results with purely hand-crafted ICD-9-CM classifiers. Our best model got a 90.26 % F measure on the training dataset and an 88.93 % F measure on the challenge test dataset, using the micro-averaged Fβ=1 measure, the official evaluatio
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