Histopathological diagnosis of colon cancer using micro-FTIR hyperspectral imaging and deep learning.

Background And Objective: Current studies based on digital biopsy images have achieved satisfactory results in detecting colon cancer despite their limited visual spectral range. Such methods may be less accurate when applied to samples taken from the tumor margin region or to samples containing multiple diagnoses. In contrast with the traditional computer vision approach, micro-FTIR hyperspectral images quantify the tissue-light interaction on a histochemical level and characterize different tissue pathologies, as they present a unique spectral signature.

Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography.

The prediction and detection of radiation-related caries (RRC) are crucial to manage the side effects of the head and the neck cancer (HNC) radiotherapy (RT). Despite the demands for the prediction of RRC, no study proposes and evaluates a prediction method. This study introduces a method based on artificial intelligence neural network to predict and detect either regular caries or RRC in HNC patients under RT using features extracted from panoramic radiograph.

A similarity-based data warehousing environment for medical images.

A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them.

A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.

Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images.

BioBankWarden: A web-based system to support translational cancer research by managing clinical and biomaterial data.

Background: Researchers of translational medicine face numerous challenges in attempting to bring research results to the bedside. This field of research covers a wide range of resources, including blood and tissue samples, which are processed for isolation of RNA and DNA to study cancer omics data (genomics, proteomics and metabolomics). Clinical information about patients׳ habits, family history, physical examinations, remissions, etc.

Two-dimensional sample entropy analysis of rat sural nerve aging.

Entropy analysis of images are usually performed using Shannon entropy, which calculates the probability of occurrency of each gray level on the image. However, not only the pixel gray level but also the spatial distribution of pixels might be important for image analysis. On the other hand, sample entropy (SampEn) is an important tool for estimation of irregularity in time series, which calculates the probability of pattern occurrence within the series.

Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization.

Background: Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists of a new method that applies fuzzy logic concepts to improve the representation of features related to image description in order to make it semantically more consistent. Specifically, we have developed a computer-aided diagnosis tool for automatic BI-RADS categorization of breast lesions.

Computational framework to support integration of biomolecular and clinical data within a translational approach.

Background: The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data.

A new family of distance functions for perceptual similarity retrieval of medical images.

A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes.