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.

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.

Bangladesh is the fourth largest rice-producing country in the world. Agriculture plays a vital role in the country's economy. One of the major obstacles in rice production is rice paddy diseases. In this paper, we develop a deep learning-based system to detect rice paddy diseases. In the first step, a rice paddy image dataset is analyzed and preprocessed for classification. To build the classifier, we use the Efficient Net B3 Convolution Neural Network (CNN) model. Next, we train a new model using segmented rice paddy disease-affected areas to detect affected regions using MASK Recurrent Convolutional Neural Network (Mask RCNN). For the classification methods, we obtain an accuracy of nearly ~99%. For segmentation, the loss value of the class, bounding box, and mask are 0.09, 0.29, 0.30. The mean Average Precision(mAP) of the segmentation is around ~89%.