USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution

Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. However, the high cost of annotation and labeling makes it challenging to achieve accurate results. We propose incorporating active learning into dense regression models to address this problem. Active learning allows models to select the most informative samples for labeling, reducing the overall annotation cost while improving performance. Despite its potential, active learning has not been widely explored in high-dimensional computer vision regression tasks like super-resolution. We address this research gap and propose a new framework called USIM-DAL that leverages the statistical properties of colour images to learn informative priors using probabilistic deep neural networks that model the heteroscedastic predictive distribution allowing uncertainty quantification. Moreover, the aleatoric uncertainty from the network serves as a proxy for error that is used for active learning. Our experiments on a wide variety of datasets spanning applications in natural images (visual genome, BSD100), medical imaging (histopathology slides), and remote sensing (satellite images) demonstrate the efficacy of the newly proposed USIM-DAL and superiority over several dense regression active learning methods.Comment: Accepted at UAI 202

Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models

Trusting the predictions of deep learning models in safety critical settings such as the medical domain is still not a viable option. Distentangled uncertainty quantification in the field of medical imaging has received little attention. In this paper, we study disentangled uncertainties in image to image translation tasks in the medical domain. We compare multiple uncertainty quantification methods, namely Ensembles, Flipout, Dropout, and DropConnect, while using CycleGAN to convert T1-weighted brain MRI scans to T2-weighted brain MRI scans. We further evaluate uncertainty behavior in the presence of out of distribution data (Brain CT and RGB Face Images), showing that epistemic uncertainty can be used to detect out of distribution inputs, which should increase reliability of model outputs

A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology

In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table

Recent Progress in Transformer-based Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments