Adversarial Machine Learning For Advanced Medical Imaging Systems

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems. We start by describing our advanced medical imaging systems to tackle the major challenges of on-device deployment: automation, uncertainty, and resource constraint. It is followed by novel unsupervised and semi-supervised robust training schemes to enhance the adversarial robustness of these medical imaging systems. These methods are designed to tackle the unique challenges of defending against adversarial attacks on medical imaging systems and are sufficiently flexible to generalize to various medical imaging modalities and problems. We continue on developing novel training scheme to enhance adversarial robustness of the general DNN based natural image classification models. Based on a unique insight into the predictive behavior of DNNs that they tend to misclassify adversarial samples into the most probable false classes, we propose a new loss function as a drop-in replacement for the cross-entropy loss to improve DNN\u27s adversarial robustness. Specifically, it enlarges the probability gaps between true class and false classes and prevents them from being melted by small perturbations. Finally, we conclude the dissertation by summarizing original contributions and discussing our future work that leverages DNN interpretability constraint on adversarial training to tackle the central machine learning problem of generalization gap

Image acquisition and storage for medical imaging systems

Image Acquisition and Storage for Medical Imaging Systems investigates the issues and requirements to develop a medical imaging system for the dental industry. Research was conducted through studying image acquisition and digitization systems, image file format standards, and data image distribution techniques in a medical facility. Furthermore, the future trends in medical imaging industry were identified; From the studies gathered, a medical imaging system called Miniature Image and Data Acquisition System (MIDAS) was created. MIDAS is an intraoral camera imaging system, which has the capability to capture images of patient\u27s teeth and gums, track images with patient data, and distributes images and data over a Local Area Network (LAN). These capabilities match or exceed those found in most intraoral camera systems

Integration of digital watermarking technique into medical imaging systems

This paper presents the process of integrating digital watermarking technique into medical imaging workflow to evaluate, validate and verify its applicability and appropriateness to medical domains. This is significant to ensure the ability of the proposed approach to tackle security threats that may face medical images during routine medical practices. This work considers two key objectives within the aim of defining a secure and practical digital medical imaging system: current digital medical workflows are deeply analyzed to define security limitations in Picture Archiving and Communication Systems (PACS) of medical imaging; the proposed watermarking approach is then theoretically tested and validated in its ability to operate in a real-world scenario (e.g. PACS). These have been undertaken through identified case studies related to manipulations of medical images within PACS workflow during acquisition, viewing, exchanging and archiving. This work assures the achievement of the identified particular requirements of digital watermarking when applied to digital medical images and also provides robust controls within medical imaging pipelines to detect modifications that may be applied to medical images during viewing, storing and transmitting

The use of steerable channels for detecting asymmetrical signals with random orientations

In the optimization of medical imaging systems, there is a stringent need to shift from human observer studies to numerical observer studies, because of both cost and time limitations. Numerical models give an objective measure for the quality of displayed images for a given task and can be designed to predict the performance of medical specialists performing the same task. For the task of signal detection, the channelized Hotelling observer (CHO) has been successfully used, although several studies indicate an overefficiency of the CHO compared to human observers. One of the main causes of this overefficiency is attributed to the intrinsic uncertainty about the signal (such as its orientation) that a human observer is dealing with. Deeper knowledge of the discrepancies of the CHO and the human observer may provide extra insight in the processing of the human visual system and this knowledge can be utilized to better fine-tune medical imaging systems

Growth of quantum three-dimensional structure of InGaAs emitting at ~1 µm applicable for a broadband near-infrared light source

We obtained a high-intensity and broadband emission centered at ~1 µm from InGaAs quantum three-dimensional (3D) structures grown on a GaAs substrate using molecular beam epitaxy. An InGaAs thin layer grown on GaAs with a thickness close to the critical layer thickness is normally affected by strain as a result of the lattice mismatch and introduced misfit dislocations. However, under certain growth conditions for the In concentration and growth temperature, the growth mode of the InGaAs layer can be transformed from two-dimensional to 3D growth. We found the optimal conditions to obtain a broadband emission from 3D structures with a high intensity and controlled center wavelength at ~1 µm. This method offers an alternative approach for fabricating a broadband near-infrared light source for telecommunication and medical imaging systems such as for optical coherence tomography

A domain specific language for performance evaluation of medical imaging systems

We propose iDSL, a domain specific language and toolbox for performance evaluation of Medical Imaging Systems. iDSL provides transformations to MoDeST models, which are in turn converted into UPPAAL and discrete-event MODES models. This enables automated performance evaluation by means of model checking and simulations. iDSL presents its results visually. We have tested iDSL on two example image processing systems. iDSL has successfully returned differentiated delays, resource utilizations and delay bounds. Hence, iDSL helps in evaluating and choosing between design alternatives, such as the effects of merging subsystems onto one platform or moving functionality from one platform to another

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements