PDF(Pubmed)
Abstract：
UNASSIGNED: The objective of the study is to evaluate the performance of CNN-based proposed models for predicting patients\' response to NAC treatment and the disease development process in the pathological area. The study aims to determine the main criteria that affect the model\'s success during training, such as the number of convolutional layers, dataset quality and depended variable.
UNASSIGNED: The study uses pathological data frequently used in the healthcare industry to evaluate the proposed CNN-based models. The researchers analyze the classification performances of the models and evaluate their success during training.
UNASSIGNED: The study shows that using deep learning methods, particularly CNN models, can offer strong feature representation and lead to accurate predictions of patients\' response to NAC treatment and the disease development process in the pathological area. A model that predicts \'miller coefficient\', \'tumor lymph node value\', \'complete response in both tumor and axilla\' values with high accuracy, which is considered to be effective in achieving complete response to treatment, has been created. Estimation performance metrics have been obtained as 87%, 77% and 91%, respectively.
UNASSIGNED: The study concludes that interpreting pathological test results with deep learning methods is an effective way of determining the correct diagnosis and treatment method, as well as the prognosis follow-up of the patient. It provides clinicians with a solution to a large extent, particularly in the case of large, heterogeneous datasets that can be challenging to manage with traditional methods. The study suggests that using machine learning and deep learning methods can significantly improve the performance of interpreting and managing healthcare data.
UNASSIGNED: The study uses pathological data frequently used in the healthcare industry to evaluate the proposed CNN-based models. The researchers analyze the classification performances of the models and evaluate their success during training.
UNASSIGNED: The study shows that using deep learning methods, particularly CNN models, can offer strong feature representation and lead to accurate predictions of patients\' response to NAC treatment and the disease development process in the pathological area. A model that predicts \'miller coefficient\', \'tumor lymph node value\', \'complete response in both tumor and axilla\' values with high accuracy, which is considered to be effective in achieving complete response to treatment, has been created. Estimation performance metrics have been obtained as 87%, 77% and 91%, respectively.
UNASSIGNED: The study concludes that interpreting pathological test results with deep learning methods is an effective way of determining the correct diagnosis and treatment method, as well as the prognosis follow-up of the patient. It provides clinicians with a solution to a large extent, particularly in the case of large, heterogeneous datasets that can be challenging to manage with traditional methods. The study suggests that using machine learning and deep learning methods can significantly improve the performance of interpreting and managing healthcare data.
摘要：
■该研究的目的是评估基于CNN的拟议模型的性能，以预测患者对NAC治疗的反应以及病理区域的疾病发展过程。本研究旨在确定在训练过程中影响模型成功的主要标准,例如卷积层的数量，数据集质量和因变量。
■该研究使用医疗保健行业中经常使用的病理数据来评估拟议的基于CNN的模型。研究人员分析了模型的分类性能，并在训练过程中评估了它们的成功。
■研究表明，使用深度学习方法，特别是CNN模型，可以提供强大的特征表示，并导致患者对NAC治疗的反应和病理区域的疾病发展过程的准确预测。预测“米勒系数”的模型，'肿瘤淋巴结值',“肿瘤和腋下的完全反应”值具有很高的准确性，这被认为是有效实现对治疗的完全反应，已经创建。估计性能指标为87%，77%和91%，分别。
■该研究得出结论，用深度学习方法解释病理检查结果是确定正确诊断和治疗方法的有效方法，以及患者的预后随访。它在很大程度上为临床医生提供了解决方案，特别是在大型的情况下，异构数据集，使用传统方法管理可能具有挑战性。该研究表明，使用机器学习和深度学习方法可以显着提高解释和管理医疗保健数据的性能。
■该研究使用医疗保健行业中经常使用的病理数据来评估拟议的基于CNN的模型。研究人员分析了模型的分类性能，并在训练过程中评估了它们的成功。
■研究表明，使用深度学习方法，特别是CNN模型，可以提供强大的特征表示，并导致患者对NAC治疗的反应和病理区域的疾病发展过程的准确预测。预测“米勒系数”的模型，'肿瘤淋巴结值',“肿瘤和腋下的完全反应”值具有很高的准确性，这被认为是有效实现对治疗的完全反应，已经创建。估计性能指标为87%，77%和91%，分别。
■该研究得出结论，用深度学习方法解释病理检查结果是确定正确诊断和治疗方法的有效方法，以及患者的预后随访。它在很大程度上为临床医生提供了解决方案，特别是在大型的情况下，异构数据集，使用传统方法管理可能具有挑战性。该研究表明，使用机器学习和深度学习方法可以显着提高解释和管理医疗保健数据的性能。
