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Min Feng

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2 papers

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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

Exponential distance transform maps for cell localization

Bo Li
Jie Chen
Hang Yi
Min Feng
Yongquan Yang
Qikui Zhu
Hong Bu

Cell localization in medical image analysis aims for precise identification of cell positions. Existing methods involve predicting density maps from images, followed by post-processing to extract cell location and number details. The quality of generated density maps significantly impacts the model’s localization and counting performance. However, density maps produced with Gaussian kernels exhibit stacking in dense regions, resulting in inaccurate cell location information and suboptimal localization performance. In this study, we propose an exponential distance transform map that ensures accurate location information and provides well-defined gradient details for effective model learning, setting a new benchmark for high performance. Additionally, to address the challenge of substantial variations in cell color within images, we introduce a multi-scale gradient aggregation module that enhances the model’s color recognition robustness through gradient information utilization. Experimental results across diverse datasets showcase notable improvements, establishing a novel benchmark for cell localization.

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