CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images

Due to the lack of automated methods, to diagnose cerebrovascular disease, time-of-flight magnetic resonance angiography (TOF-MRA) is assessed visually, making it time-consuming. The commonly used encoder-decoder architectures for cerebrovascular segmentation utilize redundant features, eventually leading to the extraction of low-level features multiple times. Additionally, convolutional neural networks (CNNs) suffer from performance degradation when the batch size is small, and deeper networks experience the vanishing gradient problem. Methods: In this paper, we attempt to solve these limitations and propose the 3D cerebrovascular attention UNet method, named CV-AttentionUNet, for precise extraction of brain vessel images. We proposed a sequence of preprocessing techniques followed by deeply supervised UNet to improve the accuracy of segmentation of the brain vessels leading to a stroke. To combine the low and high semantics, we applied the attention mechanism. This mechanism focuses on relevant associations and neglects irrelevant anatomical information. Furthermore, the inclusion of deep supervision incorporates different levels of features that prove to be beneficial for network convergence. Results: We demonstrate the efficiency of the proposed method by cross-validating with an unlabeled dataset, which was further labeled by us. We believe that the novelty of this algorithm lies in its ability to perform well on both labeled and unlabeled data with image processing-based enhancement. The results indicate that our method performed better than the existing state-of-the-art methods on the TubeTK dataset. Conclusion: The proposed method will help in accurate segmentation of cerebrovascular structure leading to stroke

Gene Transformer: Transformers for the Gene Expression-based Classification of Cancer Subtypes

Adenocarcinoma and squamous cell carcinoma constitute approximately 40% and 30% of all lung cancer subtypes, respectively, and display broad heterogeneity in terms of clinical and molecular responses to therapy. Molecular subtyping has enabled precision medicine to overcome these challenges and provide significant biological insights to predict prognosis and improve clinical decision making. Over the past decade, conventional ML algorithms and DL-based CNNs have been espoused for the classification of cancer subtypes from gene expression datasets. However, these methods are potentially biased toward identification of cancer biomarkers. Recently proposed transformer-based architectures that leverage the self-attention mechanism encode high throughput gene expressions and learn representations that are computationally complex and parametrically expensive. However, compared to the datasets for natural language processing applications, gene expression consists of several hundreds of thousands of genes from a limited number of observations, making it difficult to efficiently train transformers for bioinformatics applications. Hence, we propose an end-to-end deep learning approach, Gene Transformer, which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant biomarkers across multiple cancer subtypes without requiring feature selection as a prerequisite for the current classification algorithms. The proposed architecture achieved an overall improved performance for all evaluation metrics and had fewer misclassification errors than the commonly used traditional classification algorithms. The classification results show that Gene Transformer can be an efficient approach for classifying cancer subtypes, indicating that any improvement in deep learning models in computational biology can also be reflected well in this domain.