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Chengyang Zhang

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3 papers

2 author rows

JBHI Journal 2024 Journal Article

Difference-Deformable Convolution With Pseudo Scale Instance Map for Cell Localization

Chengyang Zhang
Jie Chen
Bo Li
Min Feng
Yongquan Yang
Qikui Zhu
Hong Bu Bu

Cell localization still faces two unresolved challenges: 1) the dramatic variations in cell morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) existing cell location maps lack scale information, resulting in insufficient supervision for point maps and inaccurate supervision for density maps. 1) To address the first challenges, we introduce a novel gradient-aware and shape-adaptive Difference-Deformable Convolution (DDConv), which enhances the model's robustness to color by leveraging gradient information while adaptively adjusting the shape of the convolutional kernel to tackle the substantial variability in cell morphology. 2) To overcome the issue of unreasonable location maps, we propose the Pseudo-Scale Instance (PSI) map, which can adaptively provide the corresponding scale information for each cell to realize accurate supervision. We analyze and evaluate DDConv and the PSI map in three challenging cell localization tasks. In comparison to existing methods, our proposed approach significantly enhances localization performance, setting a new benchmark for the cell localization task.

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EAAI Journal 2024 Journal Article

Lite-UNet: A lightweight and efficient network for cell localization

Bo Li
Yong Zhang
Yunhan Ren
Chengyang Zhang
Baocai Yin

Cell localization constitutes a fundamental research domain within the realm of pathology image analysis, with its core objective being the precise identification of cell spatial coordinates. The task has always involved the challenge of large color variations among cells, uneven distribution, and overlapping borders. Furthermore, in realistic cell localization scenarios, the existing state-of-the-art methods suffer from high computational costs and slow inference times, which severely reduce the efficiency of computer-assisted. To tackle the above issues, a lightweight and efficient cell localization model named Lite-UNet is proposed. Specifically, the Lite-UNet encompasses three pivotal modules. Firstly, we introduce a gradient aggregation module grounded in difference convolution. This module effectively mitigates the challenge posed by extensive color variations among cells by adeptly leveraging gradient information. Secondly, we propose an efficient plug-and-play graph correlation attention module, which optimizes the feature representation capabilities by encoding higher-order feature associations. Finally, we design a lightweight Ghost_CBAM module that alleviates the difficulty of uneven cell distribution while forming the base module of the Lite-UNet. Extensive experiments show that our Lite-UNet is capable of locating cells in images quickly and accurately, thus further improving the efficiency of computer-assisted medicine.

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IROS Conference 2022 Conference Paper

Vision-Assisted Localization and Terrain Reconstruction with Quadruped Robots

Chengyang Zhang
Jiashi Zhang
Jun Wu 0003
Qiuguo Zhu

Legged robots, specifically quadruped robots, have good locomotion performance in complex and rugged terrain and are becoming widely used in field exploration and rescue missions. To achieve full autonomy in such scenarios, robots need not only accurate localization but also an accurate understanding of the surrounding terrain, which will be used for robots path planning and foothold planning. However, due to the kinetic characteristic and limitation of size, quadruped robots have the disadvantages of high-frequency jitter and limited field of sensors, which lead to some challenges in environmental perception. In this paper, we propose a vision-assisted rugged terrain environment reconstruction and localization method for quadruped robots. We use a depth camera to assist in the generation of high-precision localization and terrain reconstruction results, which can help achieve the autonomous mobility of quadruped robots in this environment. We test our method on a quadruped robot platform. Our experimental results show less error and lower drift in different stairs terrain types than the commonly used lidar-based localization method.

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