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Xuanya Li

7 accepted papers

2023

Automatic Segmentation of Nasopharyngeal Carcinoma in CT Images Using Dual Attention and Edge Detection

ICASSP 2023accepted

Nasopharyngeal carcinoma (NPC) is a malignant tumor with a high incidence. Accurate segmentation of the tumor region in Computed Tomography (CT) images of NPC is the key to treatment. However, the features of uneven grayscale values and hazy boundaries of NPC regions make accurate NPC segmentation p…

Cited by 0SourceScholar
2023

Boundary Cue Guidance and Contextual Feature Mining for Glass Segmentation

ICASSP 2023accepted

Glass is ubiquitous in the real world, and its perception has many applications, including robot navigation and drone tracking. However, due to the transparent property of glass, the interior of a glass area can be any surrounding scene or object, which brings challenges for computer vision. Inspire…

Cited by 0SourceScholar
2023

Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation

ICASSP 2023accepted

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the…

Cited by 0SourceScholar
2023

Multi-Object Localization and Irrelevant-Semantic Separation for Nuclei Segmentation in Histopathology Images

ICASSP 2023accepted

Automated segmentation of nuclei in histopathology images is critical for cancer diagnosis and prognosis. Due to the high variability of nuclei morphology, numerous nuclei overlapping, and the wide existence of nuclei clusters, this task still remains challenging. In this paper, we propose an effect…

Cited by 0SourceScholar
2023

Transwnet: Integrating Transformers into CNNS via Row and Column Attention for Abdominal Multi-Organ Segmentation

ICASSP 2023accepted

Learning how to model global relationships and extract local details is crucial in improving the performance of multi-organ segmentation. Most existing U-shaped structure methods use feature fusion to address these two challenges, but still lack the ability to balance capturing global relationships…

Cited by 0SourceScholar
2022

A Novel Convolutional Neural Network Based on Adaptive Multi-Scale Aggregation and Boundary-Aware for Lateral Ventricle Segmentation on MR images

ICASSP 2022accepted

In this paper, we propose a novel convolutional neural network based on adaptive multi-scale feature aggregation and boundary-aware for lateral ventricle segmentation (MB-Net), which mainly includes three parts, i.e., an adaptive multi-scale feature aggregation module (AMSFM), an embedded boundary r…

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2021

A Hybrid Feature Enhancement Method for Gl And Segmentation In Histopathology Images

ICASSP 2021accepted

Accurate and automatic gland segmentation can help pathologists diagnose the malignancy of colorectal cancers. However, it remains a challenging task because of the large morphological differences between the glands and the presence of sticky glands. In this paper, a hybrid feature enhancement netwo…

Cited by 0SourceScholar