A neural network for glomerulus classification based on histological images of kidney biopsy.

BACKGROUND: Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. RESULTS: We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). CONCLUSIONS: Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.

Face Recognition, Musical Appraisal, and Emotional Crossmodal Bias.

Recent research on the crossmodal integration of visual and auditory perception suggests that evaluations of emotional information in one sensory modality may tend toward the emotional value generated in another sensory modality. This implies that the emotions elicited by musical stimuli can influence the perception of emotional stimuli presented in other sensory modalities, through a top-down process. The aim of this work was to investigate how crossmodal perceptual processing influences emotional face recognition and how potential modulation of this processing induced by music could be influenced by the subject's musical competence. We investigated how emotional face recognition processing could be modulated by listening to music and how this modulation varies according to the subjective emotional salience of the music and the listener's musical competence. The sample consisted of 24 participants: 12 professional musicians and 12 university students (non-musicians). Participants performed an emotional go/no-go task whilst listening to music by Albeniz, Chopin, or Mozart. The target stimuli were emotionally neutral facial expressions. We examined the N170 Event-Related Potential (ERP) and behavioral responses (i.e., motor reaction time to target recognition and musical emotional judgment). A linear mixed-effects model and a decision-tree learning technique were applied to N170 amplitudes and latencies. The main findings of the study were that musicians' behavioral responses and N170 is more affected by the emotional value of music administered in the emotional go/no-go task and this bias is also apparent in responses to the non-target emotional face. This suggests that emotional information, coming from multiple sensory channels, activates a crossmodal integration process that depends upon the stimuli emotional salience and the listener's appraisal.