This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers.
Fully convolutional network is a powerful tool for per-pixel semantic segmentation/detection.
Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer.
First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues.
Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality.
BA2 aims to obtain the optimal area under the ROC curve (AUC) by constraining the reject rates of the positive and negative classes respectively.
In this survey paper, we classify the existing methods by their principles and discuss the current research status and point out the future research trend in-depth.
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems.
Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches.
And the dynamic features are formed by using the information of its neighbor frames in the sequence.