Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning

This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints, with the aim to produce more discriminant object features for the identification of the type of kidney stones seen in endoscopic images. The model was further improved with a two-step transfer learning approach and by attention blocks to refine the learned feature maps. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 6% in terms of accuracy of the kidney stones classification.Comment: This paper has been accepted at the LatinX in Computer Vision (LXCV) Research workshop at ICCV 2023 (Paris, France

A Novel Framework for Fast Feature Selection Based on Multi-Stage Correlation Measures

Datasets with thousands of features represent a challenge for many of the existing learning methods because of the well known curse of dimensionality. Not only that, but the presence of irrelevant and redundant features on any dataset can degrade the performance of any model where training and inference is attempted. In addition, in large datasets, the manual management of features tends to be impractical. Therefore, the increasing interest of developing frameworks for the automatic discovery and removal of useless features through the literature of Machine Learning. This is the reason why, in this paper, we propose a novel framework for selecting relevant features in supervised datasets based on a cascade of methods where speed and precision are in mind. This framework consists of a novel combination of Approximated and Simulate Annealing versions of the Maximal Information Coefficient (MIC) to generalize the simple linear relation between features. This process is performed in a series of steps by applying the MIC algorithms and cutoff strategies to remove irrelevant and redundant features. The framework is also designed to achieve a balance between accuracy and speed. To test the performance of the proposed framework, a series of experiments are conducted on a large battery of datasets from SPECTF Heart to Sonar data. The results show the balance of accuracy and speed that the proposed framework can achieve

Boosting kidney stone identification in endoscopic images using two-step transfer learning

Published in MICAI 2023: Advances in Soft Computing, Lecture Notes in Computer Science book series, LNAI, volume 14392, pp.131-141, Springer, Cham, 2023International audienceBoosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning Francisco Lopez-Tiro, Daniel Flores-Araiza, Juan Pablo Betancur-Rengifo, Ivan Reyes-Amezcua, Jacques Hubert, Gilberto Ochoa-Ruiz & Christian Daul Conference paper First Online: 09 November 2023 101 AccessesPart of the Lecture Notes in Computer Science book series (LNAI,volume 14392)AbstractKnowing the cause of kidney stone formation is crucial to establish treatments that prevent recurrence. There are currently different approaches for determining the kidney stone type. However, the reference ex-vivo identification procedure can take up to several weeks, while an in-vivo visual recognition requires highly trained specialists. Machine learning models have been developed to provide urologists with an automated classification of kidney stones during an ureteroscopy; however, there is a general lack in terms of quality of the training data and methods. In this work, a two-step transfer learning approach is used to train the kidney stone classifier. The proposed approach transfers knowledge learned on a set of images of kidney stones acquired with a CCD camera to a final model that classifies images from endoscopic images. The results show that learning features from different domains with similar information helps to improve the performance of a model that performs classification in real conditions (for instance, uncontrolled lighting conditions and blur). Finally, in comparison to models that are trained from scratch or by initializing ImageNet weights, the obtained results suggest that the two-step approach extracts features improving the identification of kidney stones in endoscopic images

Volcàn de Colima dome collapse of July, 2015 and associated pyroclastic density currents

During July 10th–11th 2015, Volcán de Colima, Mexico, underwent its most intense eruptive phase since its Subplinian–Plinian 1913 AD eruption. Production of scoria coincident with elevated fumarolic activity and SO2 flux indicate a significant switch of upper-conduit dynamics compared with the preceding decades of dome building and vulcanian explosions. A marked increase in rockfall events and degassing activity was observed on the 8th and 9th of July. On the 10th at 20:16 h (Local time = UTM − 6 h) a partial collapse of the dome generated a series of pyroclastic density currents (PDCs) that lasted 52 min and reached 9.1 km to the south of the volcano. The PDCs were mostly channelized by the Montegrande and San Antonio ravines, and produced a deposit with an estimated volume of 2.4 × 106 m3. Nearly 16 h after the first collapse, a second and larger collapse occurred which last 1 h 47 min. This second collapse produced a series of PDCs along the same ravines, reaching a distance of 10.3 km. The total volume calculated for the PDCs of the second event is 8.0 × 106 m3. Including associated ashfall deposits, the two episodes produced a total of 14.2 × 106 m3 of fragmentary material. The collapses formed an amphitheater-shaped crater open towards the south. We propose that the dome collapse was triggered by arrival of gas-rich magma to the upper conduit, which then boiled-over and sustained the PDCs. A juvenile scoria sample selected from the second partial dome collapse contains hornblende, yet at an order of magnitude less abundant (0.2%) than that of 1913, and exhibits reaction rims, whereas the 1913 hornblende is unreacted. At present there is no compelling petrologic evidence for imminent end-cycle activity observed at Volcán de Colima