We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: a) It classifies the video frames into abnormal or normal using a Weakly Supervised Convolutional Neural Network (WCNN) architecture; b) detects salient points from deeper WCNN layers, using a Deep Saliency Detection (DSD) algorithm; and c) localizes GI anomalies using an Iterative Cluster Unification (ICU) algorithm. ICU is based on a Pointwise Cross-Feature-Map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The datasets used include a variety of GI anomalies. Both the anomaly detection and the localization performance achieved, in terms of the Area Under receiver operating Characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.

Medical decision making can be regarded as a process, combining both analytical cognition and intuition. It involves reasoning within complex causal models of multiple concepts, usually described by uncertain, imprecise, and/or incomplete information. Aiming to model medical decision making, we propose a novel approach based on cognitive maps and intuitionistic fuzzy logic. The new model, called intuitionistic fuzzy cognitive map (iFCM), extends the existing fuzzy cognitive map (FCM) by considering the expert's hesitancy in the determination of the causal relations between the concepts of a domain. Furthermore, a modification in the formulation of the new model makes it even less sensitive than the original model to missing input data. To validate its effectiveness, an iFCM with 34 concepts representing fuzzy, linguistically expressed patient-specific data, symptoms, and multimodal measurements was constructed for pneumonia severity assessment. The results obtained reveal its comparative advantage over the respective FCM model by providing decisions that match better with the ones made by the experts. The generality of the proposed approach suggests its suitability for a variety of medical decision-making tasks.

Uncertainty and imprecision characterize human cognitive and reasoning processes. Fuzzy cognitive maps (FCMs) are computationally simple yet effective structures to approximately model and simulate such processes. A limitation of current FCMs is that they are unable to model the hesitancy introduced into a complex system due to imperfect facts, missing information, and indecision. To cope with this issue, we propose a novel extension of the FCM model which is based on the theory of intuitionistic fuzzy sets. This intuitionistic FCM (iFCM) model, which is denoted as iFCM-II, inherently exploits the mathematical framework of intuitionistic fuzzy sets for the definition of the concepts constituting the cognitive map and their interrelations, as well as for reasoning. Furthermore, unlike the previous iFCM model, which is denoted as iFCM-I, it enables an intuitionistic estimation of hesitancy at the output concepts, thus offering a natural mechanism to assess the quality of its output. The advantages of the proposed iFCM model over the current FCM and iFCM models are demonstrated with reproducible numeric examples for process control and decision support applications.