A deep ensemble medical image segmentation with novel sampling method and loss function.

Medical image segmentation is a critical task in computer vision because of facilitating precise identification of regions of interest in medical images. This task plays an important role in disease diagnosis and treatment planning. In recent years, deep learning algorithms have exhibited remarkable performance in this domain. However, it is important to note that there are still unresolved issues, including challenges related to class imbalance and achieving higher levels of accuracy. Considering the challenges, we propose a novel approach to the semantic segmentation of medical images. In this study, a new sampling method to handle class imbalance in the medical datasets is proposed that ensures a comprehensive understanding of both abnormal tissues and background characteristics. Additionally, we propose a novel loss function inspired by exponential loss, which operates at the pixel level. To enhance segmentation performance further, we present an ensemble model comprising two UNet models with ResNet backbone. The initial model is trained on the primary dataset, while the second model is trained on the dataset obtained through our sampling method. The predictions of both models are combined using an ensemble model. We have assessed the effectiveness of our approach using three publicly available datasets: Kvasir-SEG, FLAIR MRI Low-Grade Glioma (LGG), and ISIC 2018 datasets. In our evaluation, we have compared the performance of our loss function against four different loss functions. Furthermore, we have showcased the excellence of our approach by comparing it with various state-of-the-art methods.

AEVAE: Adaptive Evolutionary Autoencoder for Anomaly Detection in Time Series.

Anomaly detection (AD) has witnessed substantial advancements in recent years due to the increasing need for identifying outliers in various engineering applications that undergo environmental adaptations. Consequently, researchers have focused on developing robust AD methods to enhance system performance. The primary challenge faced by AD algorithms lies in effectively detecting unlabeled abnormalities. This study introduces an adaptive evolutionary autoencoder (AEVAE) approach for AD in time-series data. The proposed methodology leverages the integration of unsupervised machine learning techniques with evolutionary intelligence to classify unlabeled data. The unsupervised learning model employed in this approach is the AE network. A systematic programming framework has been devised to transform AEVAE into a practical and applicable model. The primary objective of AEVAE is to detect and predict outliers in time-series data from unlabeled data sources. The effectiveness, speed, and functionality enhancements of the proposed method are demonstrated through its implementation. Furthermore, a comprehensive statistical analysis based on performance metrics is conducted to validate the advantages of AEVAE in terms of unsupervised AD.

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.

In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model is trained on a CT scan dataset and tested on another CT scan dataset, it predicts near-random results. To address this, data from several different sources can be combined using transfer learning, taking into account the intrinsic and natural differences in existing datasets obtained with different medical imaging tools and approaches. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model, namely AMTLDC. In AMTLDC, representations are learned that are similar among the sources. In other words, extracted representations are general and not dependent on the particular dataset domain. We apply the AMTLDC to predict Covid-19 from medical images using a convolutional neural network. We show that accuracy can be improved using the AMTLDC framework, and surpass the results of current successful transfer learning approaches. In particular, we show that the AMTLDC works well when using different dataset domains, or when there is insufficient data.

Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method.

Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has generated significant interest and desire in recent years and extensive research due to its low cost and speed of operation. As a result, it is critical to develop an intelligent classification system for drug proteins. This study proposes two distinct models for the prediction of druggable protein classes based on the deep learning method. The translation of drug-protein sequences is based on six physicochemical properties of amino acids. Following the application of the autocovariance method, converted sequences are used as fixed-length input vectors in deep stacked sparse auto-encoders (DSSAEs) network. The coded protein sequences are also considered and utilized as a six-channel input vector for the deep convolutional neural network model. The experimental results contributing to the deep convolution model are more efficient than previous studies for classifying druggable proteins. The proposed approach achieved a sensitivity of 96.92%, a specificity of 99.51%, and an accuracy of 98.29%.