Improved DeTraC Binary Coyote Net-Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole-Slide Pathological Images

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-11-15 DOI:10.1002/rcs.70009

M. Ramkumar, R. Sarath Kumar, R. Padmapriya, S. Balu Mahandiran

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引用次数: 0

Abstract

Background

Early detection of lymph node metastasis in breast cancer is vital for improving treatment outcomes and prognosis.

Methods

This study introduces an Improved Decompose, Transfer, and Compose Binary Coyote Net-based Multiple Instance Learning (ImDeTraC-BCNet-MIL) method for predicting lymph node metastasis from Whole Slide Images (WSIs) using multiple instance learning. The method involves segmenting WSIs into patches using Otsu and double-dimensional clustering techniques. The developed multiple instance learning approach introduces a paradigm into computational pathology by shaping pathological data and constructing features. ImDeTraC-BCNet-MIL was utilised for feature generation during both training and testing to differentiate lymph node metastasis in WSIs.

Results

The proposed model achieves the highest accuracy of 95.3% and 99.8%, precision values of 98% and 99.8%, and recall rates of 92.9% and 99.8% on the Camelyon16 and Camelyon17 datasets.

Conclusions

These findings underscore the effectiveness of ImDeTraC-BCNet-MIL in enhancing the early detection of lymph node metastasis in breast cancer.

查看原文本刊更多论文

基于DeTraC二元丛林狼网的改进型多实例学习，用于从整张病理图像预测乳腺癌淋巴结转移。

背景：乳腺癌淋巴结转移的早期检测对改善治疗效果和预后至关重要：乳腺癌淋巴结转移的早期检测对于改善治疗效果和预后至关重要：本研究介绍了一种基于二元丛林狼网的改进分解、转移和组合多实例学习（ImDeTraC-BCNet-MIL）方法，该方法利用多实例学习从全切片图像（WSI）中预测淋巴结转移。该方法包括使用大津聚类和双维聚类技术将 WSI 分割成斑块。所开发的多实例学习方法通过塑造病理数据和构建特征，为计算病理学引入了一种范式。在训练和测试过程中，利用 ImDeTraC-BCNet-MIL 生成特征，以区分 WSI 中的淋巴结转移：结果：所提出的模型在 Camelyon16 和 Camelyon17 数据集上的准确率分别达到 95.3% 和 99.8%，精确率分别达到 98% 和 99.8%，召回率分别达到 92.9% 和 99.8%：这些研究结果证明了 ImDeTraC-BCNet-MIL 在提高乳腺癌淋巴结转移早期检测方面的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.