Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identi...Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.Methods The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for classification.First,the U-net was applied to segment the image.Next,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was drawn.The third step was to extract the graph-curvature features of the histopathological image according to the MST image.Finally,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be obtained.Results During the experiment,we used various methods for comparison.In the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were used.We found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological images.In the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern feature.In the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and inception-V3.Through comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.展开更多
Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic challenges.Traditional histopathologic image classification is often inefficient and subjective.Although some hist...Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic challenges.Traditional histopathologic image classification is often inefficient and subjective.Although some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their development.To address these challenges,this paper proposes a method based on few-shot learning.Methods This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories.The model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy loss.In this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test datasets.Results Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category.We documented the model’s loss,accuracy,and the confusion matrix of the results.Additionally,we employed the t-SNE algorithm to analyze and assess the model’s classification performance.Conclusion The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot scenarios.It consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization capability.Our research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.82220108007).
文摘Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.Methods The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for classification.First,the U-net was applied to segment the image.Next,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was drawn.The third step was to extract the graph-curvature features of the histopathological image according to the MST image.Finally,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be obtained.Results During the experiment,we used various methods for comparison.In the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were used.We found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological images.In the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern feature.In the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and inception-V3.Through comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.
基金supported by National Natural Science Foundation of China(Grant No.82220108007)Liaoning Province Applied Basic Research Program(Grant No.2023JH2/101600016)+1 种基金Sichuan Science and Technology Planning Project(Grant No.2024YFHZ0320)Special Project for Traditional Chinese Medicine Research of Sichuan Administration of Traditional Chinese Medicine(Grant No.2024zd030).
文摘Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic challenges.Traditional histopathologic image classification is often inefficient and subjective.Although some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their development.To address these challenges,this paper proposes a method based on few-shot learning.Methods This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories.The model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy loss.In this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test datasets.Results Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category.We documented the model’s loss,accuracy,and the confusion matrix of the results.Additionally,we employed the t-SNE algorithm to analyze and assess the model’s classification performance.Conclusion The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot scenarios.It consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization capability.Our research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.
