Task-Guided Radial Basis Function Network for Mitosis Detection in Breast Histopathology Images

LIU Zi-yi; TANG Qi-ling; CAI Yu

doi:10.12263/DZXB.20230014

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Task-Guided Radial Basis Function Network for Mitosis Detection in Breast Histopathology Images

PAPERS | 更新时间：2025-12-08

- Task-Guided Radial Basis Function Network for Mitosis Detection in Breast Histopathology Images
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3146-3154(2023)
- 作者机构：
  
  1.中南民族大学生物医学工程学院，湖北武汉 430074
  2.青岛大学附属烟台毓璜顶医院医学工程处，山东烟台 264001
- 作者简介：
- 基金信息：
  
  Special Fund for Basic Scientific Research Business Expenses of Central Universities(CZY22014)
- DOI：10.12263/DZXB.20230014
  CLC： TP391
- Received：03 January 2023，
  
  Revised：2023-04-15，
  
  Published：25 November 2023
- 稿件说明：
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刘子仪,唐奇伶,蔡玉.任务引导的径向基网络对乳腺病理图像有丝分裂检测[J].电子学报,2023,51(11):3146-3154.

LIU Zi-yi,TANG Qi-ling,CAI Yu.Task-Guided Radial Basis Function Network for Mitosis Detection in Breast Histopathology Images[J].ACTA ELECTRONICA SINICA,2023,51(11):3146-3154.
刘子仪,唐奇伶,蔡玉.任务引导的径向基网络对乳腺病理图像有丝分裂检测[J].电子学报,2023,51(11):3146-3154. DOI： 10.12263/DZXB.20230014.

LIU Zi-yi,TANG Qi-ling,CAI Yu.Task-Guided Radial Basis Function Network for Mitosis Detection in Breast Histopathology Images[J].ACTA ELECTRONICA SINICA,2023,51(11):3146-3154. DOI： 10.12263/DZXB.20230014.

摘要

有丝分裂数目是判别乳腺癌恶性程度的重要指标，在诊断、治疗及预后中具有重要的意义.然而，在临床实践上乳腺癌切片中有丝分裂细胞的检测主要是通过病理学医生进行人工计数，这个过程繁琐耗时且具有很强主观性.本文提出了一种乳腺病理图像有丝分裂自动检测的两阶段方法.在第一个阶段，利用卷积神经网络作为主干融合深监督与注意力机制进行有丝分裂细胞候选块的定位.在第二个阶段，将获取的候选块输入融合了径向基函数网络的验证模型，进一步提高有丝细胞识别准确率.针对有丝细胞类内存在较大差异的问题，本文通过任务来引导径向基函数中心的确定，利用径向基函数的“局部响应”特性来表达有丝分裂细胞的形态多样性.通过在弱标签数据集ICPR 2014和AMIDA 2013上进行评估，本文所提出的网络模型取得了最优的

-score，与竞争方法相比，分别提高了5.4%和3.0%，从而证明了该方法对于有丝分裂检测的有效性.

Abstract

The number of mitosis is an important index to judge the malignancy of breast cancer

and it is of great significance in diagnosis

treatment and prognosis. However

in clinical practice

the detection of mitotic cells in breast cancer histopathology slices is mainly performed by manual counting by pathologists. This process is tedious

time-consuming and highly subjective. In this paper

we propose a two-stage method for automatic detection of mitosis in breast histopathology images

. In the first stage

the localization of mitotic cell candidate patches is performed using a convolutional neural network as the backbone with deep supervision and attention mechanisms. In the second stage

the generated candidate patches are input into the verification model of radial basis function network to further improve the accuracy of mitotic cell identification. Aiming at the problem of large differences within mitotic cell types

this paper uses tasks to guide the definition of the radial basis function centers and uses the “local response” property of radial basis function to express the morphological diversity of mitotic cells. By evaluating on the weak label datasets ICPR 2014 and AMIDA 2013

the proposed network model has achieved the best

-score

which is 5.4% and 3.0% higher than the existing methods

thus proving that the validity of the method for mitosis detection.

关键词

Keywords

references

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