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Background Approximately one‐fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective. Purpose To develop a deep‐learning‐based approach for finding brain metastasis on MRI. Study Type Retrospective. Sequence Axial postcontrast 3D T1‐weighted imaging. Field Strength 1.5T and 3T. Population A total of 361 scans of 121 patients were used to train and test the Faster region‐based convolutional neural network (Faster R‐CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R‐CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing. Assessment Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2‐step pipeline consisting of a Faster R‐CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false‐positive foci detected. Statistical Tests The performance of the algorithm was evaluated by using sensitivity, false‐positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per‐metastases and per‐slice. Results Testing on held‐out brain MRI data demonstrated 96% sensitivity and 20 false‐positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false‐positive metastases per slice. The area under the ROC curve was 0.79. Conclusion Our results showed that deep‐learning‐based computer‐aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2020;52:1227–1236.

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We study the problem of utilizing human intelligence to categorize a large number of objects. In this problem, given a category hierarchy and a set of objects, we can ask humans to check whether an object belongs to a category, and our goal is to find the most cost-effective strategy to locate the appropriate category in the hierarchy for each object, such that the cost (i.e., the number of questions to ask humans) is minimized. There are many important applications of this problem, including image classification and product categorization. We develop an online framework, in which category distribution is gradually learned and thus an effective order of questions are adaptively determined. We prove that even if the true category distribution is known in advance, the problem is computationally intractable. We develop an approximation algorithm, and prove that it achieves an approximation factor of 2. We also show that there is a fully polynomial time approximation scheme for the problem. Furthermore, we propose an online strategy which achieves nearly the same performance guarantee as the offline optimal strategy, even if there is no knowledge about category distribution beforehand. Experiments on a real crowdsourcing platform demonstrate the effectiveness of our method.

Abstract-Applications of metallic metamaterials have generated significant interest in recent years. Electromagnetic behavior of metamaterials in the optical range is usually characterized by a locallinear response. In this article, we develop a finite-difference time-domain (FDTD) solution of the hydrodynamic model that describes a free electron gas in metals. Extending beyond the locallinear response, the hydrodynamic model enables numerical investigation of nonlocal and nonlinear interactions between electromagnetic waves and metallic metamaterials. By explicitly imposing the current continuity constraint, the proposed model is solved in a self-consistent manner. Charge, energy and angular momentum conservation laws of high-order harmonic generation have been demonstrated for the first time by the Maxwell-hydrodynamic FDTD model. The model yields nonlinear optical responses for complex metallic metamaterials irradiated by a variety of waveforms. Consequently, the multiphysics model opens up unique opportunities for characterizing and designing nonlinear nanodevices.