Label-free cell classification is advantageous for supplying pristine cells for further use or examination,yet existing techniques frequently fall short in terms of specificity and speed.In this study,we address these...Label-free cell classification is advantageous for supplying pristine cells for further use or examination,yet existing techniques frequently fall short in terms of specificity and speed.In this study,we address these limitations through the development of a novel machine learning framework,Multiplex Image Machine Learning(MIML).This architecture uniquely combines label-free cell images with biomechanical property data,harnessing the vast,often underutilized biophysical information intrinsic to each cell.By integrating both types of data,our model offers a holistic understanding of cellular properties,utilizing cell biomechanical information typically discarded in traditional machine learning models.This approach has led to a remarkable 98.3%accuracy in cell classification,a substantial improvement over models that rely solely on image data.MIML has been proven effective in classifying white blood cells and tumor cells,with potential for broader application due to its inherent flexibility and transfer learning capability.It is particularly effective for cells with similar morphology but distinct biomechanical properties.This innovative approach has significant implications across various fields,from advancing disease diagnostics to understanding cellular behavior.展开更多
Multi-instance multi-label learning(MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels.During the past few years,many MIML algo...Multi-instance multi-label learning(MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels.During the past few years,many MIML algorithms have been developed and many applications have been described.However,there lacks theoretical exploration to the learnability of MIML.In this paper,through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels,we show that the MIML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable.展开更多
文摘Label-free cell classification is advantageous for supplying pristine cells for further use or examination,yet existing techniques frequently fall short in terms of specificity and speed.In this study,we address these limitations through the development of a novel machine learning framework,Multiplex Image Machine Learning(MIML).This architecture uniquely combines label-free cell images with biomechanical property data,harnessing the vast,often underutilized biophysical information intrinsic to each cell.By integrating both types of data,our model offers a holistic understanding of cellular properties,utilizing cell biomechanical information typically discarded in traditional machine learning models.This approach has led to a remarkable 98.3%accuracy in cell classification,a substantial improvement over models that rely solely on image data.MIML has been proven effective in classifying white blood cells and tumor cells,with potential for broader application due to its inherent flexibility and transfer learning capability.It is particularly effective for cells with similar morphology but distinct biomechanical properties.This innovative approach has significant implications across various fields,from advancing disease diagnostics to understanding cellular behavior.
基金supported by the National Basic Research Program of China(2010CB327903)the National Natural Science Foundation of China(61073097,61021062)
文摘Multi-instance multi-label learning(MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels.During the past few years,many MIML algorithms have been developed and many applications have been described.However,there lacks theoretical exploration to the learnability of MIML.In this paper,through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels,we show that the MIML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable.
