Communication-theoretic Approach for Skin Cancer Detection using Dynamic Thermal Imaging

Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Statistics from the Americans Cancer Society indicate that 20% of the American population will develop this disease during their lifetime. Presently, visual inspection by a dermatologist has good sensitivity (\u3e90%) but poor specificity (\u3c10%), especially for melanoma conditions, which is the most dangerous type of skin cancer with a five-year survival rate between 16-62%. Over the past few decades, several studies have evaluated the use of infrared imaging to diagnose skin cancer. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging and processing technique to diagnose melanoma and non-melanoma skin cancer lesions. In DTI, the suspicious lesion is cooled down and the thermal recovery of the skin is monitored with an infrared camera. The proposed algorithm exploits the intrinsic order present in the time evolution of the thermal recoveries of the skin of human subjects to diagnose the malignancy and it achieves outstanding performance for discriminating between benign and malignant skin lesions. In this dissertation we propose a stochastic parametric representation of the thermal recovery curve, which is extracted from a heat equation. The statistics of the random parameters associated with the proposed stochastic model are estimated from measured thermal recovery curves of subjects with known condition. The stochastic model is, in turn, utilized to derive an analytical autocorrelation function (ACF) of the stochastic recovery curves. The analytical ACF is utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed algorithm was tested in a pilot study including 140 human subjects and we have demonstrated sensitivity in excess of 99% for a prescribed false-alarm probability of 1% (specificity in excess of 99%) for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method. The proposed algorithm is studied in details for different patient permutations demonstrating robustness in maximizing the probability of detecting those subjects with malignant condition. Moreover, the proposed method is further generalized to include thermal recovery curves of the tissue that surrounds the suspicious lesion as a local reference. Such a local reference permits the compensation of any possible anomalous behavior in the lesion thermal recovery, which, in turn, improves both the theoretical and empirical performance of the method. As a final contribution, we develop a novel edge-detection algorithm--specifically targeted for multispectral (MS) and hyperspectral (HS) imagery--which performs l edge detection based solely on spectral (color) information. More precisely, this algorithm fuses the process of detecting edges through ratios of pixels with critical information resulting from spectral classification of the very image whose edges are to be identified. This algorithm is tested in multicolor (spectral) imagery achieving superior results as compared with other alternatives. The edge-detection algorithm is subsequently utilized in the skin-cancer detection context to define the lesion boundary from a visible color image by exploiting the color contrast between the pigmented tissue and the surrounding skin. With this automated lesion selection, we develop a method to extract spatial features equivalent to those utilized by the dermatologists in diagnosing malignant conditions. These spatial features are fused with the temporal features, obtained from the thermal-recovery method, to yield a spatio-temporal method for skin-cancer detection. While providing a rigorous mathematical foundation for the viability of the dynamic thermal recovery approach for skin-cancer detection, the research completed in this dissertation also provides the first reliable, accurate and non-invasive diagnosis method for preliminary skin-cancer detection. This dissertation, therefore, paves the way for future clinical studies to produce new skin-cancer diagnosis practices that minimize the need for unnecessary biopsies without sacrificing reliability

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Hierarchical representations for spatio-temporal visual attention: modeling and understanding

Mención Internacional en el título de doctorDentro del marco de la Inteligencia Artificial, la Visión Artificial es una disciplina científica que tiene como objetivo simular automaticamente las funciones del sistema visual humano, tratando de resolver tareas como la localización y el reconocimiento de objetos, la detección de eventos o el seguimiento de objetos....Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Salgado Álvarez de Sotomayor.- Secretario: Ascensión Gallardo Antolín.- Vocal: Jenny Benois Pinea

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all