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Due to superior soft tissue contrast afforded by magnetic resonance imaging (MRI), there is great potential for multi-parametric MRI (mpMRI) for the detection and eventual classification of renal masses (RMs). In this study, we investigated fully automated deep learning methods for RMs detection using T2-Weighted (T2W) spin-echo and two contrast-enhanced T1-Weighted gradient-echo-corticomedullary (T1W-CM), nephrographic-phase (T1W-NG), T1-Weighted In-phase (T1W-IP) and opposed-phase (T1W-OP) images. The dataset contained mpMRI images of 108 kidney cancer patients with an average size of renal mass of $24~\pm ~7.8$ cm. In the first stage, kidneys were segmented using a 2D attention U-Net model, which was reported in a previous study. In the second stage, we tested five different state-of-the-art methods for RMs detections on mpMRI sequences. The model predictions were compared to manual annotations using precision, recall, specificity, and Dice Similarity Coefficient (DSC). The best-performing deep learning models were U-Net, U-Net++, and attention U-Net on the T2W, T1W-CM, and T1W-NG sequences respectively. Of the 5 mpMRI sequences, we also demonstrated that the T1W-CM is the most suitable for RMs detection. This automated detection of RMs in mpMRI sequences may be useful for the subsequent characterization of RMs in a fully automated artificial intelligence-based pipeline.

In this paper, the problem of scheduling in a two-stage supply chain system is investigated with considering coordinated scheduling of different stages. The objective is to find the similar sequences of processing tasks in two steps with two aims of minimizing the total preparation time as well as the maximum preparation time of each stage. To do so, the problem has been solved by non-dominant sorting genetic algorithm (NSGA-II) and using a variety of multi-objective techniques. The proposed meta-heuristic algorithm is applied for the first time in this problem. The results show that the proposed algorithm is able to find Pareto optimal solutions with a high efficiency.