A Multi-Layer Feature Fusion Method for Few-Shot Image Classification.

In image classification, few-shot learning deals with recognizing visual categories from a few tagged examples. The degree of expressiveness of the encoded features in this scenario is a crucial question that needs to be addressed in the models being trained. Recent approaches have achieved encouraging results in improving few-shot models in deep learning, but designing a competitive and simple architecture is challenging, especially considering its requirement in many practical applications. This work proposes an improved few-shot model based on a multi-layer feature fusion (FMLF) method. The presented approach includes extended feature extraction and fusion mechanisms in the Convolutional Neural Network (CNN) backbone, as well as an effective metric to compute the divergences in the end. In order to evaluate the proposed method, a challenging visual classification problem, maize crop insect classification with specific pests and beneficial categories, is addressed, serving both as a test of our model and as a means to propose a novel dataset. Experiments were carried out to compare the results with ResNet50, VGG16, and MobileNetv2, used as feature extraction backbones, and the FMLF method demonstrated higher accuracy with fewer parameters. The proposed FMLF method improved accuracy scores by up to 3.62% in one-shot and 2.82% in five-shot classification tasks compared to a traditional backbone, which uses only global image features.

Parametric evaluation of impedance curve in radiofrequency ablation: A quantitative description of the asymmetry and dynamic variation of impedance in bovine ex vivo model.

Radiofrequency ablation (RFA) is a treatment for liver tumors with advantages over the traditional treatment of surgical resection. This procedure has the shortest recovery time in early stage tumors. The objective of this study is to parameterize the impedance curve of the RFA procedure in an ex vivo model by defining seven parameters (t1/2, tminimum, tend, Zinitial, Z1/2, Zminimum and Zend). Based on these parameters, three performance indices are defined: one to identify the magnitude of impedance curve asymmetry (δ), one Drop ratio (DR) describing the percentage of impedance decrease until the minimum impedance point is reached, and Ascent Ratio (AR) describing the magnitude of increase in impedance from the minimum impedance point to its maximum point. Fifty ablations were performed in a bovine ex vivo model to measure and evaluate the proposed parameters and performance index. The results show that the groups had an average δ of 29.02%, DR of 22.41%, and AR of 545.33% for RFA without the use of saline or deionized solutions. The saline solution and deionized water-cooled groups indicated the correlation of performance indices δ, DR, and AR with the obtained final ablation volume. Therefore, by controlling these parameters and indices, lower recurrence is achieved.