Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology.

Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra.

The estimation of skin optical properties by means of inverse problem solving from spatially resolved diffuse reflectance (SR-DR) spectra is one way to exploit the acquired clinical signals. This method requires the comparison between the experimental spectra collected with a medical device, and spectra generated by the photons transport numerical simulations. This comparison is usually limited to spectral shape due to the absence of intensity standardization of the experimental DR spectra.

Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique.

This study presents the results of the classification of diffuse reflectance (DR) spectra and multiexcitation autofluorescence (AF) spectra that were collected in vivo from precancerous and benign skin lesions at three different source detector separation (SDS) values. Spectra processing pipeline consisted of dimensionality reduction, which was performed using principal component analysis (PCA), followed by classification step using such methods as support vector machine (SVM), multilayered perceptron (MLP), linear discriminant analysis (LDA), and random forest (RF). In order to increase the efficiency of lesion classification, several data fusion methods were applied to the classification results: majority voting, stacking, and manual optimization of weights.

Evaluation and understanding of automated urinary stone recognition methods.

Objective: To assess the potential of automated machine-learning methods for recognizing urinary stones in endoscopy.

Materials And Methods: Surface and section images of 123 urinary calculi (109 ex vivo and 14 in vivo stones) were acquired using ureteroscopes. The stones were more than 85% 'pure'.