Confident texture-based laryngeal tissue classification for early stage diagnosis support

Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range ðIQRÞ ¼ 6%]. When excluding low-confidence patches, the achieved median recall was increased to 98% (IQR ¼ 5%), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis

Confident texture-based laryngeal tissue classification for early stage diagnosis support

none8siopenMoccia, Sara; De Momi, Elena; Guarnaschelli, Marco; Savazzi, Matteo; Laborai, Andrea; Guastini, Luca; Peretti, Giorgio; Mattos, Leonardo S.Moccia, Sara; De Momi, Elena; Guarnaschelli, Marco; Savazzi, Matteo; Laborai, Andrea; Guastini, Luca; Peretti, Giorgio; Mattos, Leonardo S

Supervised cnn strategies for optical image segmentation and classification in interventional medicine

The analysis of interventional images is a topic of high interest for the medical-image analysis community. Such an analysis may provide interventional-medicine professionals with both decision support and context awareness, with the final goal of improving patient safety. The aim of this chapter is to give an overview of some of the most recent approaches (up to 2018) in the field, with a focus on Convolutional Neural Networks (CNNs) for both segmentation and classification tasks. For each approach, summary tables are presented reporting the used dataset, involved anatomical region and achieved performance. Benefits and disadvantages of each approach are highlighted and discussed. Available datasets for algorithm training and testing and commonly used performance metrics are summarized to offer a source of information for researchers that are approaching the field of interventional-image analysis. The advancements in deep learning for medical-image analysis are involving more and more the interventional-medicine field. However, these advancements are undeniably slower than in other fields (e.g. preoperative-image analysis) and considerable work still needs to be done in order to provide clinicians with all possible support during interventional-medicine procedures

Helping a CBR Program Know What it Knows

Case-based reasoning systems need to know the limitations of their expertise. Having found the known source cases most relevant to a target problem, they must assess whether those cases are similar enough to the problem to warrant venturing advice. In experimenting with SIROCCO, a twostage case-based retrieval program that uses structural mapping to analyze and provide advice on engineering ethics cases, we concluded that it would sometimes be better for the program to admit that it lacks the knowledge to suggest relevant codes and past source cases. We identified and encoded three strategic metarules to help it decide. The metarules leverage incrementally deeper knowledge about SIROCCO's matching algorithm to help the program "know what it knows." Experiments demonstrate that the metarules can improve the program's overall advice-giving performance

Helping a CBR Program Know What it Knows

Case-based reasoning systems need to know the limitations of their expertise. Having found the known source cases most relevant to a target problem, they must assess whether those cases are similar enough to the problem to warrant venturing advice. In experimenting with SIROCCO, a twostage case-based retrieval program that uses structural mapping to analyze and provide advice on engineering ethics cases, we concluded that it would sometimes be better for the program to admit that it lacks the knowledge to suggest relevant codes and past source cases. We identified and encoded three strategic metarules to help it decide. The metarules leverage incrementally deeper knowledge about SIROCCO's matching algorithm to help the program "know what it knows." Experiments demonstrate that the metarules can improve the program's overall advice-giving performance