模糊逻辑引导的多粒度深度神经网络

doi:10.16451/j.cnki.issn1003-6059.202309002

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Abstract The accurate identification and classification of pathological images are crucial for early disease detection and treatment. In the process of diagnosis, pathologists typically employ a multi-level approach, observing abnormal cell regions at various magnifications. However, existing models often extract features at a single granularity, neglecting the multi-granularity nature of cells. Therefore, a fuzzy logic guided deep neural network with multi-granularity is proposed in this paper. Firstly, multi-granularity feature extraction is conducted for cell structures at three levels of granularity-coarse, medium and fine, and thus the information in pathological tissue images is fully utilized. Additionally, to address the issue of key information redundancy during multi-granularity feature extraction, fuzzy logic theory is introduced. Multiple fuzzy membership functions are set to describe cell features from different angles. Subsequently, fuzzy operations are employed to fuse into a fuzzy universal feature. A fuzzy logic guided cross attention mechanism is designed to guide the multi-granularity features by the universal fuzzy feature. Finally, an encoder is utilized to propagate the features to all patch tokens, resulting in improved classification accuracy and robustness. Experiments demonstrate that the proposed network achieves high accuracy in the classification of pathological images.

Key words： Fuzzy Logic Medical Image Classification Feature Fusion Feature Extraction Attention Mechanism

Received: 24 July 2023

ZTFLH:

TP18

Fund:National Natural Science Foundation of China(No.61976120,62006128,62102199), Natural Science Foundation of Jiangsu Province(No.BK20231337), Key Program of Natural Science Research of Higher Education of Jiangsu Province(No.21KJA510004), General Program of Nature Science Research of Higher Education of Jiangsu Province(No.23KJB520031), China Postdoctoral Science Foundation(No.2022M711716)

Corresponding Authors: DING Weiping, Ph.D, professor. His research interests include data mining, machine learning, granular computing, evolutionary computing and big data analytics.)

About author:: ZHOU Tianyi, master student. His research interests include granular computing, fuzzy sets and deep learning.HUANG Jiashuang, Ph.D., associate professor. His research interests include brain network analysis and deep learning. JU Hengrong, Ph.D., associate professor. His research interests include granular computing, rough sets, machine learning and know-ledge discovery. JIANG Shu, Ph.D., lecturer. Her research interests include deep learning and na-tural language processing. WANG Haipeng, master student. His research include fuzzy theory, granular computing and deep learning.

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	ZHOU Tianyi
	DING Weiping
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	JIANG Shu
	WANG Haipeng

Cite this article:

ZHOU Tianyi,DING Weiping,HUANG Jiashuang等. Fuzzy Logic Guided Deep Neural Network with Multi-granularity[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(9): 778-792.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202309002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I9/778

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