Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a multimodal learning pipeline to predict PAWP marker. We utilize complementary information from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal features from CMR scans using tensor-based learning. We propose a graph attention network to select important EHR features for prediction, where we model subjects as graph nodes and feature relationships as graph edges using the attention mechanism. We design four feature fusion strategies: early, intermediate, late, and hybrid fusion. With a linear classifier and linear fusion strategies, our pipeline is interpretable. We validate our pipeline on a large dataset of ${2},{641}$ subjects from our ASPIRE registry. The comparative study against state-of-the-art methods confirms the superiority of our pipeline. The decision curve analysis further validates that our pipeline can be applied to screen a large population. The code is available at https://github.com/prasunc/hemodynamics.

Glioma has emerged as the deadliest form of brain tumor for human beings. Timely diagnosis of these tumors is a major step towards effective oncological treatment. Magnetic Resonance Imaging (MRI) typically offers a non-invasive inspection of brain lesions. However, manual inspection of tumors from MRI scans requires a large amount of time and it is also an error-prone process. Therefore, automated diagnosis of tumors plays a crucial role in clinical management and surgical interventions of gliomas. In this study, we propose a Convolutional Neural Network (CNN)-based framework for non-invasive grading of tumors from 3D MRI scans. The proposed framework incorporates two novel CNN architectures. The first CNN architecture performs the segmentation of tumors from multimodel MRI volumes. The proposed segmentation network leverages the spatial and channel attention modules to recalibrate the feature maps across the layers. The second network utilizes the multi-task learning strategy to perform the classification based on the three glioma grading tasks which include characterization of tumor into low-grade or high-grade, identification of 1p19q, and Isocitrate Dehydrogenase (IDH) status. We have carried out several experiments to evaluate the performance of our method. Extensive experimental observations indicate that the proposed framework achieves better performance than several state-of-the-art methods. We have also executed Welch's- t test to show the statistical significance of grading results. The source code of this study is available at https://github.com/prasunc/Gliomanet.

Pulmonary Arterial Hypertension (PAH) is a lifethreatening disorder. The prediction of mortality in PAH patients can play a crucial role in the clinical management of this disease. The prediction of mortality from one modality is a difficult task that may only provide limited performance. Therefore, we propose a multimodal learning approach in this work to predict one-year mortality in PAH patients. We have utilised three modalities, which include extracted numerical imaging features, echo report categorical features, and echo report text features from Electronic Health Records (EHRs) of patients. We have proposed a feature integration module to combine features from multiple modalities. The text features have been extracted from the echo reports using the Bidirectional Encoder Representations from Transformers (BERT). An attention mechanism and a weighted summation method are also adopted during the process of feature integration. We have performed different experiments to evaluate the performance of the proposed framework for mortality prediction. The experimental results indicate that we can achieve the best AUC score of 0.89 for predicting one-year mortality by combining all three modalities. The source code of this paper is available at https://github.com/Mdnaimulislam/MultimodalTab.

Magnetic Resonance Imaging (MRI) are typically corrupted with random noise. This type of noise exhibits the characteristics of Rician distribution in MRI scans. Noise in MRI scans degrades the accuracy of manual and computerized inspection of diseases. Therefore, denoising of MRI images is an indispensable process before the clinical examination of any disease. In this article, we present a novel denoising neural network for MRI images. The proposed network contains a set of dilated convolutions for Rician noise removal. We have used hybrid dilated convolutions to overcome the gridding problem in the network. The residual learning scheme has also been utilized using a set of skip connections. A substantial amount of supervised MRI data has been developed for end-to-end training of the proposed network. Extensive experiments have been performed on synthetic and real MRI datasets to study the effectiveness of the proposed method. The experimental observations indicate that our method not only achieves promising performance but also retains prominent image information effectively.

Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.