Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. Digital colposcopy is a promising and inexpensive technology for the detection of cervical intraepithelial neoplasia. However, diagnostic sensitivity varies widely depending on the doctor expertise. Therefore, automation of this process is needed in both, detection and visualization. Colposcopies cover four steps: macroscopic view with magnifier white light, observation under green light, Hinselmann and Schiller. Also, there are transition intervals where the specialist manipulates the observed area. In this paper, we focus on the temporal segmentation of the video in these steps. Using our solution, physicians may focus on the step of interest and lesion detection tools can determine the interval to diagnose. We solved the temporal segmentation problem using Weighted Automata. Images were described by their chromacity histograms and labeled using a KNN classifier with a precision of 97%. Transition frames were recognized with a precision of 91 %.

Abstract

Abstract
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method's performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. As in many other diseases, the existence of several screening/diagnosis methods and subjective physician preferences creates a complex ecosystem for automated methods. In order to diminish the amount of labeled data from each modality/expert we propose a regularization-based transfer learning strategy that encourages source and target models to share the same coefficient signs. We instantiated the proposed framework to predict cross-modality individual risk and cross-expert subjective quality assessment of colposcopic images for different modalities. Thus, we are able to transfer knowledge gained from one expert/modality to another.

Abstract

Abstract
Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.