Medical Image Classification using Deep Learning Techniques and Uncertainty Quantification

The emergence of medical image analysis using deep learning techniques has introduced multiple challenges in terms of developing robust and trustworthy systems for automated grading and diagnosis. Several works have been presented to improve classification performance. However, these methods lack the diversity of capturing different levels of contextual information among image regions, strategies to present diversity in learning by using ensemble-based techniques, or uncertainty measures for predictions generated from automated systems. Consequently, the presented methods provide sub-optimal results which is not enough for clinical practice. To enhance classification performance and introduce trustworthiness, deep learning techniques and uncertainty quantification methods are required to provide diversity in contextual learning and the initial stage of explainability, respectively. This thesis aims to explore and develop novel deep learning techniques escorted by uncertainty quantification for developing actionable automated grading and diagnosis systems. More specifically, the thesis provides the following three main contributions. First, it introduces a novel entropy-based elastic ensemble of Deep Convolutional Neural Networks (DCNNs) architecture termed as 3E-Net for classifying grades of invasive breast carcinoma microscopic images. 3E-Net is based on a patch-wise network for feature extraction and image-wise networks for final image classification and uses an elastic ensemble based on Shannon Entropy as an uncertainty quantification method for measuring the level of randomness in image predictions. As the second contribution, the thesis presents a novel multi-level context and uncertainty-aware deep learning architecture named MCUa for the classification of breast cancer microscopic images. MCUa consists of multiple feature extractors and multi-level context-aware models in a dynamic ensemble fashion to learn the spatial dependencies among image patches and enhance the learning diversity. Also, the architecture uses Monte Carlo (MC) dropout for measuring the uncertainty of image predictions and deciding whether an input image is accurate based on the generated uncertainty score. The third contribution of the thesis introduces a novel model agnostic method (AUQantO) that establishes an actionable strategy for optimising uncertainty quantification for deep learning architectures. AUQantO method works on optimising a hyperparameter threshold, which is compared against uncertainty scores from Shannon entropy and MC-dropout. The optimal threshold is achieved based on single- and multi-objective functions which are optimised using multiple optimisation methods. A comprehensive set of experiments have been conducted using multiple medical imaging datasets and multiple novel evaluation metrics to prove the effectiveness of our three contributions to clinical practice. First, 3E-Net versions achieved an accuracy of 96.15% and 99.50% on invasive breast carcinoma dataset. The second contribution, MCUa, achieved an accuracy of 98.11% on Breast cancer histology images dataset. Lastly, AUQantO showed significant improvements in performance of the state-of-the-art deep learning models with an average accuracy improvement of 1.76% and 2.02% on Breast cancer histology images dataset and an average accuracy improvement of 5.67% and 4.24% on Skin cancer dataset using two uncertainty quantification techniques. AUQantO demonstrated the ability to generate the optimal number of excluded images in a particular dataset

AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

Deep learning algorithms have the potential to automate the examination of medical images obtained in clinical practice. Using digitized medical images, convolution neural networks (CNNs) have demonstrated their ability and promise to discriminate among different image classes. As an initial step towards explainability in clinical diagnosis, deep learning models must be exceedingly precise, offering a measure of uncertainty for their predictions. Such uncertainty-aware models can help medical professionals in detecting complicated and corrupted samples for re-annotation or exclusion. This paper proposes a new model and data-agnostic mechanism, called Actionable Uncertainty Quantification Optimization (AUQantO) to improve the performance of deep learning architectures for medical image classification. This is achieved by optimizing the hyperparameters of the proposed entropy-based and Monte Carlo (MC) dropout uncertainty quantification techniques escorted by single- and multi-objective optimization methods, abstaining from the classification of images with a high level of uncertainty. This helps in improving the overall accuracy and reliability of deep learning models. To support the above claim, AUQantO has been validated with four deep learning architectures on four medical image datasets and using various performance metric measures such as precision, recall, Area Under the Receiver Operating Characteristic (ROC) Curve score (AUC), and accuracy. The study demonstrated notable enhancements in deep learning performance, with average accuracy improvements of 1.76% and 2.02% for breast cancer histology and 5.67% and 4.24% for skin cancer datasets, utilizing two uncertainty quantification techniques, and AUQantO further improved accuracy by 1.41% and 1.31% for brain tumor and 4.73% and 1.83% for chest cancer datasets while allowing exclusion of images based on confidence levels

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field

MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multilevel Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models

Medical Image Classification using Deep Learning Techniques and Uncertainty Quantification

The emergence of medical image analysis using deep learning techniques has introduced multiple challenges in terms of developing robust and trustworthy systems for automated grading and diagnosis. Several works have been presented to improve classification performance. However, these methods lack the diversity of capturing different levels of contextual information among image regions, strategies to present diversity in learning by using ensemble-based techniques, or uncertainty measures for predictions generated from automated systems. Consequently, the presented methods provide sub-optimal results which is not enough for clinical practice. To enhance classification performance and introduce trustworthiness, deep learning techniques and uncertainty quantification methods are required to provide diversity in contextual learning and the initial stage of explainability, respectively. This thesis aims to explore and develop novel deep learning techniques escorted by uncertainty quantification for developing actionable automated grading and diagnosis systems. More specifically, the thesis provides the following three main contributions. First, it introduces a novel entropy-based elastic ensemble of Deep Convolutional Neural Networks (DCNNs) architecture termed as 3E-Net for classifying grades of invasive breast carcinoma microscopic images. 3E-Net is based on a patch-wise network for feature extraction and image-wise networks for final image classification and uses an elastic ensemble based on Shannon Entropy as an uncertainty quantification method for measuring the level of randomness in image predictions. As the second contribution, the thesis presents a novel multi-level context and uncertainty-aware deep learning architecture named MCUa for the classification of breast cancer microscopic images. MCUa consists of multiple feature extractors and multi-level context-aware models in a dynamic ensemble fashion to learn the spatial dependencies among image patches and enhance the learning diversity. Also, the architecture uses Monte Carlo (MC) dropout for measuring the uncertainty of image predictions and deciding whether an input image is accurate based on the generated uncertainty score. The third contribution of the thesis introduces a novel model agnostic method (AUQantO) that establishes an actionable strategy for optimising uncertainty quantification for deep learning architectures. AUQantO method works on optimising a hyperparameter threshold, which is compared against uncertainty scores from Shannon entropy and MC-dropout. The optimal threshold is achieved based on single- and multi-objective functions which are optimised using multiple optimisation methods. A comprehensive set of experiments have been conducted using multiple medical imaging datasets and multiple novel evaluation metrics to prove the effectiveness of our three contributions to clinical practice. First, 3E-Net versions achieved an accuracy of 96.15% and 99.50% on invasive breast carcinoma dataset. The second contribution, MCUa, achieved an accuracy of 98.11% on Breast cancer histology images dataset. Lastly, AUQantO showed significant improvements in performance of the state-of-the-art deep learning models with an average accuracy improvement of 1.76% and 2.02% on Breast cancer histology images dataset and an average accuracy improvement of 5.67% and 4.24% on Skin cancer dataset using two uncertainty quantification techniques. AUQantO demonstrated the ability to generate the optimal number of excluded images in a particular dataset

Spot urinary sodium for assessing dietary sodium restriction in cirrhotic ascites

AIM: To evaluate the accuracy of spot urinary Na/K and Na/creatinine (Cr) ratios as an alternative to 24-h urinary sodium in monitoring dietary compliance in patients with liver cirrhosis and ascites treated with diuretics