Yangyang Sun

Xiangyu Zhao, Peng Zhang, Fan Song et al.

Coronavirus Disease 2019 (COVID-19) has caused great casualties and becomes almost the most urgent public health events worldwide. Computed tomography (CT) is a significant screening tool for COVID-19 infection, and automated segmentation of lung infection in COVID-19 CT images will greatly assist diagnosis and health care of patients. However, accurate and automatic segmentation of COVID-19 lung infections remains to be challenging. In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above. We introduce a dilated convolution module in model decoder to achieve large receptive field, which refines decoding process and contributes to segmentation accuracy. Also, we present a dual attention mechanism composed of two attention modules which are inserted to skip connection and model decoder respectively. The dual attention mechanism is utilized to refine feature maps and reduce semantic gap between different levels of the model. The proposed method has been evaluated on open-source dataset and outperforms cutting edges methods in semantic segmentation. Our proposed D2A U-Net with pretrained encoder achieves a Dice score of 0.7298 and recall score of 0.7071. Besides, we also build a simplified D2A U-Net without pretrained encoder to provide a fair comparison with other models trained from scratch, which still outperforms popular U-Net family models with a Dice score of 0.7047 and recall score of 0.6626. Our experiment results have shown that by introducing dilated convolution and dual attention mechanism, the number of false positives is significantly reduced, which improves sensitivity to COVID-19 lesions and subsequently brings significant increase to Dice score.

Zheyuan Zhu, Yangyang Sun, Johnathon White et al.

Signal retrieval from a series of indirect measurements is a common task in many imaging, metrology and characterization platforms in science and engineering. Because most of the indirect measurement processes are well-described by physical models, signal retrieval can be solved with an iterative optimization that enforces measurement consistency and prior knowledge on the signal. These iterative processes are time-consuming and only accommodate a linear measurement process and convex signal constraints. Recently, neural networks have been widely adopted to supersede iterative signal retrieval methods by approximating the inverse mapping of the measurement model. However, networks with deterministic processes have failed to distinguish signal ambiguities in an ill-posed measurement system, and retrieved signals often lack consistency with the measurement. In this work we introduce a variational generative model to capture the distribution of all possible signals, given a particular measurement. By exploiting the known measurement model in the variational generative framework, our signal retrieval process resolves the ambiguity in the forward process, and learns to retrieve signals that satisfy the measurement with high fidelity in a variety of linear and nonlinear ill-posed systems, including ultrafast pulse retrieval, coded aperture compressive video sensing and image retrieval from Fresnel hologram.