Research frontiers of electroporation-based applications in cancer treatment: a bibliometric analysis.

OBJECTIVES: Electroporation, the breakdown of the biomembrane induced by external electric fields, has increasingly become a research hotspot for its promising related methods in various kinds of cancers. CONTENT: In this article, we utilized CiteSpace 6.1.R2 to perform a bibliometric analysis on the research foundation and frontier of electroporation-based applications in cancer therapy. A total of 3,966 bibliographic records were retrieved from the Web of Science Core Collection for the bibliometric analysis. Sersa G. and Mir L. M. are the most indispensable researchers in this field, and the University of Ljubljana of Slovenia is a prominent institution. By analyzing references and keywords, we found that, with a lower recurrence rate, fewer severe adverse events, and a higher success rate, irreversible electroporation, gene electrotransfer, and electrochemotherapy are the three main research directions that may influence the future treatment protocol of cancers. SUMMARY: This article visualized relevant data to synthesize scientific research on electroporation-based cancer therapy, providing helpful suggestions for further investigations on electroporation. OUTLOOK: Although electroporation-based technologies have been proven as promising tools for cancer treatment, its radical mechanism is still opaque and their commercialization and universalization need further efforts from peers.

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review.

Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches.

Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification.

In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF)-based framework, which integrates a semisupervised deep learning and a probabilistic graphical model, and make three contributions. First, we design four types of convolutional and transposed convolutional layers that consider the characteristics of HSIs to help with extracting discriminative features from limited numbers of labeled HSI samples. Second, we construct semisupervised generative adversarial networks (GANs) to alleviate the shortage of training samples by adding labels to them and implicitly reconstructing real HSI data distribution through adversarial training. Third, we build dense conditional random fields (CRFs) on top of the random variables that are initialized to the softmax predictions of the trained GANs and are conditioned on HSIs to refine classification maps. This semisupervised framework leverages the merits of discriminative and generative models through a game-theoretical approach. Moreover, even though we used very small numbers of labeled training HSI samples from the two most challenging and extensively studied datasets, the experimental results demonstrated that spectral-spatial GAN-CRF (SS-GAN-CRF) models achieved top-ranking accuracy for semisupervised HSI classification.