A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector embedding-based keyword extraction, and clustering. The elements of our model have been integrated and further developed to meet better the requirements of efficient information extraction, topic modeling of the extracted information, and user needs. Furthermore, our system can achieve better results than this task's existing topic modeling and keyword extraction solutions. Our approach is validated and compared with other state-of-the-art methods using publicly available datasets for benchmarking

Improving the accuracy of optic disc detection by finding maximal weighted clique of multiple candidates of individual detectors

In this paper, we propose a method to improve the automatic detection of the optic disc on fundus images. We have studied and implemented some of the optic disc detectors from concerning literature to organize them into an ensemble system. As a former work, we proposed an ensemble-based optic disc detection system, based on simple majority voting which already outperformed the individual detectors. To improve further the performance of the ensemble-based system, now we examine how we can extract more candidates from the individual algorithms to have the appropriate location of the optic disc among them. We also assign weights to each candidate based on the priority suggested by the algorithms. We consider these weighted candidates as vertices of a graph and look for a subgraph with a maximal sum of weights constrained by the geometry of the optic disc. Experimental results are also presented to see the improvement. Index Terms — optic disc, fundus image, graph 1

Composing Diverse Ensembles of Convolutional Neural Networks by Penalization

Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members’ diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions

Visualization of Fibroid in Laparoscopy Videos using Ultrasound Image Segmentation and Augmented Reality

Though they rarely become malignant, the surgical removal of fibroids (uterine myomas) is commonly considered to prevent any possible future risks. As the least invasive intervention, endoscopic surgery is the most popular approach for this aim. However, since these compact tumors reside in the deep (muscle/connective) tissues of the uterus, they are hardly visible using only the video stream provided by the endoscopic camera. Thus, conform to the current general trend in human surgery, in this paper we propose a multimodal approach to make these tumors more visible during endoscopic interventions, namely, the reconstruction of the whole three-dimensional model of the uterus from ultrasound images and segmentation of the fibroids using this modality. Then, we map the result of the segmentation on the surface of the uterus, hence they become visible during endoscopic surgery. Similar efforts have already been made, considering the usage of MRI for this purpose, but ultrasound image acquisition is more widely available, faster, and cheaper next to the lower image quality. Our aim is to use the output of the 3D ultrasound imaging device during the laparoscopic surgery. Our segmentation pipeline processes the ultrasound images and consists of Otsu's thresholding using a special mask derived from image averages and morphological snakes to extract uterus boundary. As the final step, we project the segmented 3D model of the uterus with its lesion on an endoscope camera flow in real time to provide an augmented reality application

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

International audienc