Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dim...Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.展开更多
The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing diffi...The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing difficulties,organ failure,and death.Thus,the early detection of the virus is very crucial.COVID-19 can be detected using clinical tests,making us need to know the most important symptoms/features that can enhance the decision process.In this work,we propose a modified multilayer perceptron(MLP)with feature selection(MLPFS)to predict the positive COVID-19 cases based on symptoms and features from patients’electronic medical records(EMR).MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance.Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy.Experiments were conducted using three different COVID-19 datasets and eight different models,including the proposed MLPFS.Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models.Additionally,it outperforms the other models in classification results as well as time.展开更多
文摘Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.
文摘The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing difficulties,organ failure,and death.Thus,the early detection of the virus is very crucial.COVID-19 can be detected using clinical tests,making us need to know the most important symptoms/features that can enhance the decision process.In this work,we propose a modified multilayer perceptron(MLP)with feature selection(MLPFS)to predict the positive COVID-19 cases based on symptoms and features from patients’electronic medical records(EMR).MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance.Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy.Experiments were conducted using three different COVID-19 datasets and eight different models,including the proposed MLPFS.Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models.Additionally,it outperforms the other models in classification results as well as time.
