AutoMO-Mixer: An automated multi-objective multi-layer perspecton Mixer model for medical image based diagnosis

1 INTRODUCTION . With the development of modern medicine , medical image has become an essential way to carry out personalized and accurate diagnosis . Due to the outstanding image analysis ability , deep learning has been widely used in medical image based diagnosis and has achieved great success ( Zhang & An , 2017 ) ( Shen et al. , 2017 ) in the past years . As there are so many parameters to train in current deep learning model such as convolutional neural network ( CNN ) ( Chua , 1998 ) , a large scale dataset is always required to feed the model . In particular , the parameters in convolutional layer accounted a majority of the parameters . However , it is very difficult to collect sufficient medical image samples for a particular clinical problem in practice . Recently , a new deep learning model termed MLP-Mixer ( Mixer ) ( Tolstikhin et al. , 2021 ) which was developed based on multi-layer perception ( MLP ) was proposed . Compared with CNN , the convolutional layer is removed from Mixer , leading to decreasing the architecture parameter scale sharply and Mixer can be trained sufficiently through small scale dataset . On the other hand , Mixer can achieve similar performance with CNN ( Tolstikhin et al. , 2021 ) . In summary , Mixer is a better architecture for building medical image based diagnostic model . Furthermore , building the balanced model between sensitivity and specificity is necessary ( Banerjee et al. , 2018 ) ( Mazurowski et al. , 2008 ) in clinical diagnosis . Sensitivity indicates the proportion of people who are actually sick that are diagnosed as sick , whilst specificity indicates the proportion of people who are truly healthy who are diagnosed as healthy ( Reitsma et al. , 2005 ) . As shown in Table 1 , although the accuracy is high , sensitivity and specificity imbalance , resulting in higher rate of missed diagnosis . Therefore , a more balanced model is necessary and a multi-objective model which considers sensitivity and specificity simultaneously is needed . As such , a new automated multi-objective Mixer ( AutoMO-Mixer ) model is developed in this study . In AutoMO-Mixer , both sensitivity and specificity are considered as the objective functions simultaneously and a Pareto-optimal model set can be obtained through the multi-objective optimization ( Zhou et al. , 2017 ) in training stage . In addition , since there are several hyperparameters which may affect the model performance in AutoMO-Mixer , Bayesian optimization ( Pelikan , 2005 ) is used to train the hyperparameters . In testing stage , the Pareto-optimal models with balanced sensitivity and specificity are chosen so as to improve model diversity and stability . To obtain more reliable result , evidential reasoning ( ER ) ( Yang & Xu , 2002 ) approach is used to fuse the output of selected Pareto-optimal models to obtain final outcome . The experimental studies on two public medical image datasets demonstrated that AutoMO-Mixer can outperform Mixer and CNN , and more balanced results can be achieved as well . 2 METHOD . 2.1 FRAMEWORK . The framework of AutoMO-Mixer is shown in figure 1 , which consists of training stage and testing stage . In training stage , medical images are fed into multiple Mixer models with randomly initialization . Then sensitivity and specificity are considered as the multi-objective functions simultaneously , and The Pareto-optimal Mixer model set can be obtained through multi-objective optimization . Meanwhile , since there are several hyperparameters which may affect the model performance , Bayesian optimization is used to optimize the hyperparameters . In testing stage , models with balanced sensitivity and specificity are fused using the ER approach to obtain more reliable final prediction model . 2.2 TRAINING STAGE . Assume α = { α1 , ... , αK } represents the hyperparameters , where K is the number of hyperparameter and β denotes the parameters in Mixer models . M = { m1 , ... , mq } represents the Mixer model , where q is the number of models . The MLP-Mixer network is a visual-oriented , all-MLP architecture as shown in figure 2 . It consists consisting of four parts ( Tolstikhin et al. , 2021 ) : splitting a picture into multiple patches shaped as a “ patches×channels ” table as an input ; converting all patches into feature embedding with a set of fully connected networks ; refining feature information through N mixer layers ; and classifying them with a full-connected layer after global average pooling . Mixer is divided into token-mixer ( feature refining along the columns direction of the table , i.e . spatial locations ) and channel-mixer ( feature refining along the rows direction of the table , i.e . channels ) , all using MLP for feature extraction , each MLP consists of two layers of full-connected and an activation function GELU . Since there are two groups ’ parameters to train , the training stage is a bilevel iterative optimization process . In other words , Mixers ’ model parameters and hyperparameters in AutoMO-Mixer are updated iteratively . The training stage is shown in figure 3 . In the beginning , all the parameters are initialized randomly . When the hyperparameters are determined , the Mixers ’ model parameters are trained first . To obtain the balanced model , sensitivity and specificity are considered as objective functions simultaneously , they are : fsen = TP TP + FN ( 1 ) fspe = TN TN + FP ( 2 ) where TP is true positive , TN is true negative , FP is false positive , FN is false negative . The goal is to maximize fsen , fspe simultaneously , that is : f = max β ( fsen , fspe ) ( 3 ) To optimize the above function , an iterative multi-objective immune algorithm ( IMIA ) ( Zhou et al. , 2017 ) ( Gong et al. , 2008 ) is used . IMIA consists of six steps : initialization , cloning , mutation , deletion , update , and termination . First , the initial solution set denoted by D ( t ) = { M1 , ... , MN } is generated , where Mi = { mi1 , ... , miq } , i = 1 , 2 , ... , N . Then the models with higher Mi = { mi1 , ... , miq } , i = 1 , 2 , ... , N will be replicated using the proportional cloning method . In the third step , a probability of mutation is randomly generated for each model , and the model performs mutation when its probability is larger than the mutation probability . After the mutation , the new models are generated . If the models have same sensitivity and specificity , only one model is remained and the all the other models are removed . Then the model size is kept through AUC based non-dominated sorting strategy . This training process will not stop until the maximum number of iterations is reached . Then the Pareto-optimal Mixer model set is generated , where the model set size is J . Now the optimal parameters for Mixer are obtained and hyperparameters are optimized . Since there are hyperparameters in Pareto-optimal model selection and fusion phase , the weight for each Pareto-optimal model should be estimated , which is denoted by wj . As the balanced model between sensitivity and specificity is desired , the ratio between them is considered , which is fsenfspe or fspe fsen . When the ratio is less than 0.5 or greater than 1 , the model is considered as extreme imbalance , setting wj as 0 . The expression of wj is as follows : wj = λ fjsen fjspe + ( 1− λ ) AUC , when 0.5 ≤ f j sen fjspe ≤ 1 λ fjspe fjsen + ( 1− λ ) AUC , when 0.5 ≤ f j spe fjsen ≤ 1 , j = 1 , 2 , ... , J , 0 Other situations ( 4 ) where λ indicates the importance of balance , and 1-λ indicates the importance of AUC . After calculating the wj for each model , the weights are normalized , that is : J∑ j=1 wj = 1 , 0 ≤ wj ≤ 1 ( 5 ) Then the Pareto-optimal models are fused through ER approach . Assume there are J models in Pareto-optimal model set , and the output probability ( normal and abnormal ) for each model is denoted by in Pj = { P 1j , P 2j } , j = 1 , 2 , ... , J , where P 1j is the output probability of abnormal , and P 2j is the output probability of normal . The final output probability P cfin , c = 1 , 2 is obtained through the ER fusion strategy ( Wang et al. , 2006 ) , that is : P cfin = ER ( P c j , wj ) , j = 1 , 2 , ... , J , c = 1 , 2 ( 6 ) where ER is : P cfin = µ× [ J∏ j=1 ( wjP c j + 1− wj ) − J∏ j=1 ( 1− wj ) ] 1− µ× [ J∏ j=1 ( 1− wj ) ] , c = 1 , 2 ( 7 ) The normalized factor µ is : µ = [ 2∑ c=1 J∏ j=1 ( wjP c j + 1− wj ) − J∏ j=1 ( 1− wj ) ] −1 ( 8 ) The hyperparameter α can be optimized through Bayesian optimization ( Pelikan , 2005 ) ( Springenberg et al. , 2016 ) , where the objective function is : fH = min α ( 1−AUC ) ( 9 ) Eqs . ( 3 ) and ( 9 ) are optimized iteratively until it reaches the termination critation . 2.3 TESTING STAGE . An illustration of testing stage is shown in figure 4 . The medical images are fed into the trained Pareto-optimal Mixer models and the output probabilities from each model can be obtained . Then ER approach is used to fuse the outputs of all the Mixer models and final output can be obtained . 3 EXPERIMENTS . 3.1 MATERIALS AND SETUP . The two public databases used in this paper are MRI images of brain tumors ( Panigrahi , 2021 ) and CT images of COVID-19 ( LuisBlanche , 2020 ) . The first dataset aims to help people for detecting brain tumors . It contains MRI scans of the brain , and each type of brain has 1500 images . We selected 900 images for modeling . The next dataset is collected from COVID19-related papers from medRxiv , bioRxiv , NEJM , JAMA , Lancet , etc . It contains 349 CT scans that are positive for COVID-19 , This dataset can be used to perform classification and automatically detect COVID-19 on CT scans . The distribution of the two types of data is shown in Table 2 . The examples of two datasets are shown in figure 5 . Before feeding the model , all the images are resized into 224 x 224 images , and each image is divided into 14 x 14 patches , where each patch size is 16 x 16 . There are two hyperparameters , Table 2 : Distribution of two types of data Train Train Test Test Abnormal Normal Abnormal normal Brain Tumor 360 360 90 90 COVID-19 280 318 69 79 Figure 5 : The examples of two datasets . MP and λ , which the range is set between 0 and 1 . The network structure contains five parameters , they are : the number of Mixer layers , the number of channels ( Hidden size C ) , the number of hidden layer neurons of token-mixing ( MLP dimension Ds ) , and the number of hidden layer neurons of channel-mixing ( MLP dimension Dc ) . The settings are shown in table 3 . In comparative study , Mixer and CNN model are evaluated . For fairness of comparison , the number of layers of the two models is required to be equal . We make the number of layers five . The four parameters of the Mixer network are set to 5 , 256 , 392 , 1024 , respectively . AUC ( Zhao et al. , 2011 ) is an ideal indicator to evaluate the reliability of the prediction results , and the larger the AUC shows that the better the classification of the model . Accuracy ( ACC ) represents the correct rate of diagnosis and is also an important indicator for evaluating the model . Therefore , sensitivity ( SEN ) , specificity ( SPE ) , AUC , ACC are used for evaluation . All the experiments are performed five times and the average results are shown .

Summary: This study is a simple combination of multiple-objective optimization and MLP Mixer on medical image application, with poor experiments to support any conclusion. Everything looks a direct application of existing technique. I don't understand why this study is submitted to a top conference.