The diagnosis of Oral Epithelial Dysplasia (OED), presents a high interobserver variability due to subjectivity in the evaluation criteria. In this work, we propose an automatic histological image classification strategy based on the multiple instance learning (MIL) approach, using VGG-16 convolutional neural networks for feature extraction. Four models were trained: two for classifying the degree of OED (mild, moderate, and severe) and two for the detection of six relevant histopathological criteria. To optimize the training process, we implemented the Black Hole metaheuristic to find the learning rate that maximizes the performance of the models. Evaluation of performance and interobserver variability was performed using Cohen’s Kappa coefficient. The results suggest that the use of MIL, together with metaheuristic optimization strategies, can consistently reproduce expert diagnostic perception.

Cardiac MRI makes it possible to explore blood flow in three orthogonal directions within the cardiovascular system, through an acquisition sequence called 4D flow MRI. This sequence has been used in recent years to diagnose complex cardiovascular diseases with high accuracy. However, it is limited by the long times required to obtain an accurate three-dimensional segmentation of a region of interest that allows quantification of a series of hemodynamic parameters. Segmentation of these images is challenging due to problems such as low signal-to-noise ratio, phase accumulation errors in the images, spatiotemporal resolution, and respiratory motion. To address this challenge, we propose a processing pipeline that uses a neural network for medical image registration and a cascade of semantic segmentation neural networks. This approach improves the segmentation of pulmonary artery branches and aortic sections into single or multiple cardiac phases, facilitating clinical analysis and research in cardiovascular disease.