Intracranial hemorrhage (ICH) is a type of stroke with a high mortality rate and failing to localize even minor ICH can put a patient's life at risk. However, its patterns are diverse in shapes and sizes and, sometimes, even hard to recognize its existence. Therefore, it is challenging to accurately detect and localize diverse ICH patterns. In this article, we propose a novel Perihematomal Edema Guided Scale Adaptive R-CNN (PESA R-CNN) for accurate segmentation of various size hemorrhages with the goal of minimizing missed hemorrhage regions. In our approach, we design a Center Surround Difference U-Net (CSD U-Net) to incorporate Perihematomal Edema (PHE) for more accurate Region of Interest (RoI) generation. We trained CSD U-Net to predict PHE and hemorrhage regions as targets in a weakly supervised manner and utilized its prediction results to generate RoI. By including more informative features of PHE around hemorrhage, this enhanced RoI generation allows a model to reduce the false-negative rate. Furthermore, these expanded RoIs are aligned with the Scale Adaptive RoI Align (SARA) module based on their size to prevent the loss of fine-scale information and small hemorrhage patterns. Each scale adaptively aligned RoI is processed with the corresponding separate segmentation network of Multi-Scale Segmentation Network (MSSN), which integrates the results from each scale's segmentation network. In experiments, our model shows significant improvement on dice coefficient (0.697) and Hausdorff distance (12.918), compared to all other segmentation models. It also minimizes the number of missing small hemorrhage regions and enhances overall segmentation performance on diverse ICH patterns.
Recent video captioning models aim at describing all events in a long video. However, their event descriptions do not fully exploit the contextual information included in a video because they lack the ability to remember information changes over time. To address this problem, we propose a novel context-aware video captioning model that generates natural language descriptions based on the improved video context understanding. We introduce an external memory, differential neural computer (DNC), to improve video context understanding. DNC naturally learns to use its internal memory for context understanding and also provides contents of its memory as an output for additional connection. By sequentially connecting DNC-based caption models (DNC augmented LSTM) through this memory information, our consecutively connected DNC architecture can understand the context in a video without explicitly searching for event-wise correlation. Our consecutive DNC is sequentially trained with its language model (LSTM) for each video clip to generate context-aware captions with superior quality. In experiments, we demonstrate that our model provides more natural and coherent captions which reflect previous contextual information. Our model also shows superior quantitative performance on video captioning in terms of BLEU (BLEU@4 4.37), METEOR (9.57), and CIDEr-D (28.08).
As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than ± 5 ° . In addition, the target temperature can be reached 19.8% faster and the power consumption can be saved up to 20.5% by the time the target temperature is achieved.
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