MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification
Authors: Zhenbin Wang
Mao Ye
Xiatian Zhu
Liuhan Peng
Liang Tian
Yingying Zhu
Venue: NeurIPS 2022 Accept (2022)
Keywords: Meta learning
Medical image classification
Domain adaptation

Abstract: In medical image analysis, we often need to build an image recognition system for a target scenario with the access to small labeled data and abundant unlabeled data, as well as multiple related models pretrained on different source scenarios. This presents the combined challenges of multi-source-free domain adaptation and semi-supervised learning simultaneously. However, both problems are typically studied independently in the literature, and how to effectively combine existing methods is non-trivial in design. In this work, we introduce a novel MetaTeacher framework with three key components: (1) A learnable coordinating scheme for adaptive domain adaptation of individual source models, (2) A mutual feedback mechanism between the target model and source models for more coherent learning, and (3) A semi-supervised bilevel optimization algorithm for consistently organizing the adaption of source models and the learning of target model. It aims to leverage the knowledge of source models adaptively whilst maximize their complementary benefits collectively to counter the challenge of limited supervision. Extensive experiments on five chest x-ray image datasets show that our method outperforms clearly all the state-of-the-art alternatives. The code is available at https://github.com/wongzbb/metateacher.


Tetaee: Coordinating Multi-Model Domain Adaptation for Medical Image Classification
Zhenbin Wang', Mao ; Zhu², Liuhan Peng", Liang Tian', Yingying Zhu
University of Electronic Science and Technology of China, Chengdu, China
University of Surrey, Guildford, UK Xinjjaan University, Crümqi, China
UUniveriity of Texas, Arlington, US
Zhenbinwang @ @oxmmil.com, cvlab ueste gmail.com, xiatian. surrey.ac..uk
Abstract
In medical image analysis, often we need to build image recognition system for a target scenario with the access to small labeled data and abundant unlabeled data, well multiple related models pretrained on different source scenarios. This presents the combined challenges of multi-source- domain adaptation and semi- supervised learning simultaneously. However, both problems are typically studied independently in the literature, and how effectively combine existing methods is non-trivial in design. In this work, we introduce novel Teacher framework with three key components: (1) ) A learnable coordinating scheme for adaptive domain adaptation of individual source models, (2) A mutual feedback mechanism between the target model and source models for more coherent learning, and (3) A semi-supervised bilevel optimization algorithm for consistently organizing the adaption of source models and the learning of target model. It aims to leverage the knowledge of source models adaptively whilst maximize their complementary benefits collectively to counter the challenge of limited supervision. Extensive experiments on five chest x-ray image datasets show that our method outperforms clearly all the state-of theee--f-thh-aaae art alternatives. The code is available at https:// github. com htom///ooggbbbmmtttaaccccer
1 Introduction
Despite the great stride made by existing deep learning methods medical image classification results [32 67). their performances often degrade drastically when applied to a new unseen scenario. This mainly due to the domain shift challenge between the training and test data, caused by different environments, different instruments, and different acquisition protocols. Unlike natural images, annotating medical images requires special clinical expertise. It is hence more difficult to obtain large-scale medical image datasets with high-quality labels at every single scenario. Domain adaptation is feasible solution, but comes with several limitations. Firstly, medical data is often under strict privacy and license constraints. That means the source domain data usually inaccessible during domain adaptation. Secondly, medical data is typically multi-labell which means that there are multiple labels for a sample, and the multiple categories are not mutually exclusive. It has more prominent different characteristics in different scenarios. Considering these practical constraints, propose a new Semi-supervised Multi-source-free Domain Adaptation (SMDA) problem setting in the context of medical image classification. Our proposed setting has three key conditions: (1) There are multiple source domain models trained on respective multi-label medical image datasets; (2) All
TTee corresponding author.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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the source domain data is inaccessible for adaptation; and (3) The target domain data has only a small number of labelled samples along with abundant unlabeled data.
In medical image classification, there are limited domain adaptation works, with a need of accessing the source domain data [5 19 24 34 45] [55 62). Further, they usually consider single source domain. On the other hand, for employing multiple source domains, existing Multi-Source Domain Adaptation (MSDA) methods typically learn a common feature space for all source and target domains (58) or use ensemble methods combined with source classifiers (8). However, all of these MSDA methods require access to the source domain data. Regarding multi-labed medical image classification, there exists a solution which extends the standard classifier network by conditional adversarial discriminator networks [46). But is still not source-free. Indeed, there have been extensive study on Source-Free Domain Adaptation (SFDA) 64]. However, they are not directly applicable to our problem. Firstly, most of them assume a single source domain (35) 644 Using a SFDA method to transfer each source domain model to the target domain separately and average their predictions, this strategy cannot reveal the complementary information between different source domains. Secondly, the source model is often domain biased. Different hospitals are featured with different populations, leading to a situation that the source datasets focus on a specific set of class labels. The existing SFDA methods can not assess the credibility of source domain model with different labels.
To address the above SMDA's limitations, employing knowledge distillation from multi-source 69 70). This forms a multi-teacher and one-student scheme. In our problem setting. a few labels of the target domain are provided to judge the credibility of multi-source models in different labels. In reality, it common to exploit a few
models to the target domain can be considered [18 42 65
labeled data in the target domain. Recent works [25 29 50 [5]] have shown that a few labeled data from the target domain can significantly improve the performance of the model. Inspired by meta-learning approaches (40 47 49), we consider bilevel optimization strategy to update both the teachers and students. This is because different models vary reliability and there is a need for optimizing the update direction for each source model. This offers opportunity of leveraging the complementary and collaboration of different source models during model optimization, critical for solving the luw-supernision challenge.
Based on the above analysis and consideration, we propose a novel framework, namely Meta Specifically, it is based on multi-teacher and one-student models. Each teacher model is pre-trained on a specific labeled source data. The student model is initialized by a randomly chosen teacher. In order to provide different update directions for multiple teachers, coordinating weight learning method is proposed to determine the contribution of each teacher for each target sample. In addition to knowledge transfer from multiple teachers, when adapting specific teacher model, we also explore the feedback from the student and other teachers in a semi-supervised meta learning manner [16[77] Unlike the previous MSDA approaches, MetaTeacher can adapt each teacher in different directions according to the learned coordinating weight. This enables us to fully use different characteristics of source models, whilst avoiding the problem of insufficient training samples for multi-label classification to some extent.
Our contributions are summarized as follows: (1) We propose new problem setting, i.e., semi- supervised multi-source- our best knowledge, our work is the first attempt at multi- multi-source-free and semi-supervised domain adaptation in the field of transfer learning.
domain adaptation for multi-label medical image classification. To
(2) A novel framework, Metahea,her, based on a multi- teacher and one-student scheme is introduced to solve the proposed SMDA problem. A mutual feedback mechanism is designed based on meta-learning between the target model and the source models for more coherent learning and adaptation. The knowledge from multiple source models are sufficiently leveraged. (3) A coordinating weight learning method is derived for dynamically revealing the performance differences of different source models over different classes. It is integrated with the semi-supervised bilevel optimization algorithm for consistently updating the teacher and student models. Extensive experiments on five well- chest radiography datasets show that approach outperforms state-of-the alternatives clearly, along with in-depth ablation studies for verifying the design of our model components.
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2 Relate Works
Unsupervised domain adaptation for medical image classification. There exist shallow UDA and deep UDA approaches in the literature. Shallow UDA approach adapts two routes, i.e,, source domain instance weighting 57) and feature transformation [4 34 All of these methods need to access source domain data. Similarly, there are also two routes for deep UDA approach. They are
domain alignment based [19 62 and pseudo-labeling based (5) The first strategy solves the UDA
problem by minimizing the domain difference between the source domain and target domain, and is currently the most popular method. Gao et al. (19) used the central moment difference matching to perform adaptation of classifying brain MRI data. The second strategy generates dummy data to retrain target model. For multi-label medical image classification, there exists work based on domain alignment with a multi-label regularization term (46). Bermúdez Chacón et al. [5) used the normalized cross correlaonnn to generate soft labels for the target domain. The above UDA methods do not update the source domain model, and they are all based on single-source domain. However, the situation of multi-source domains is very common in practical situations.
Source-free domain adaptation. Source-free free domain adaptation methods can be roughly divided into two routes, i.e., generative approach (27 28) 33 63) and pseudo-label approach [4 26|35]3551 56). The generation approach generates target-style training samples to train the prediction model. Since learning to generate features is difficult, this approach is extremely limited. The pseudo-label approach generates pseudo-labels through the source domain model, which is simple and general and has recently achieved good results in the machine learning community. The research of source-free domain adaptation the medical image analysis field mainly focuses on image segmentation. Bateson er al. (4) maximized the mutual information between the target images and their label predictions to perform spine, prostate and cardiac segmentation. Vibashan et al. [56) implemented source- free domain adaptive image segmentation by generating pseudo-labels and applied self- self-training methods for task ssecciiccificc representation. These works are all conducted in the single-soure source domain case. Currently, the research on multi-source-fie free domain adaptation is extremely limited, and most of the works adapt the method of generating trusted pseudo-labels 1.14.
Multi-source domain adaptation for medical image classification. In machine learning commu- nity, MSDA works mainly have two strategies. i.e. distribution alignment [3 74 and adversarial learning [6 72). The first strategy computes the statistical discrepancy between multi-source domains and target domain, and then combines all predictions. The second strategy trains a domain discriminator and forces the feature extraction network to learn domain-invarian features to confuse the domain discriminator. For medical image classification, there only exist several shallow DA models. Wang et al. (58) proposed to map multiple source and target data to a common latent space for autism spectrum disorder classification. Cheng el al. (8] constructed a multi-domain transfer classifier for the early diagnosis of Alzheimer's disease. All of these strategies require to access source domain data and are not suitable for solving the proposed SMDA problem. To the best of our knowledge, current ceacher-student domain adaptation methods in the medical and machine learning communitics only consider the single-source domain case. When extended to the multi-source domain, it will face a challenging multi-objeccive optimization problem 41
Semi-supervised domain adaptation (SSDA). Our problem is also related to SSDA which assumes a small number of labeled samples in the target domain. Compared to UDA, using few labeled
samples of the target domain allows to further achieve better domain alignment [31 66). Due to the shift of domain distribution, directly applying classical semi-supervised learning methods to the SSDA problem will lead to sub-optimal performance. Representative SSDA works are based
on subspace learning 44 66). entropy minimization (0). label smoothing [13 (8)48) and active
learning 48 52). However, all of these methods assume a single source domain with the source domain data accessible. Unlike these works, our method incorporates meta-learning and uses the performance on the labeled target data as a feedback signal.
Teacher-student domain adaptation models in medical image analysis. Usually, teacher-student domain adaptation model proposes multiple consistencies to solve UDA problem. To the best of our knowledge, teacher-student based domain adaptation methods have received little attention on medical image analysis. Perone el al. (45) proposed semi-supervised learning based UDA method for medical image segmentation, which minimizes the consistency loss of the predicted results between the student model and the teacher model for unlabeled samples in the target domain during the training process. The network is updated by the exponential moving average of the student
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Target labeled data
Target unlabeled data (large)
Inpur labeled data
(small)
Student
Teacher
Teacher
Student
Teacher
Teachern
Inpua unlabeled data
Probssbiltiis
nating Weight
Coordinating
Mapping
Mapping
h-
Cin Bf)
(x
C v1)
Cly
Cp
Festue Ext adoe
(a) Coordinating Weight Learning
(b) Bilevel Optimization
Figure I: Overview of our proposed MetaTeacher architecture. (a) Learning the coordinating weight mapping which will be used subsequently to provide guidance for updating the teacher models. (b) Alternately updating the teacher and student models. Each teacher updated with feedback signals from the student and other teachers.
network weights (mean-teacher (44) The method is experimentally performed the SCAM (Spinal Cord Anatomy MR Image) dataset to demonstrate its effectiveness. There are several approaches on teacher-student domain adaptation in the field of machine learning. French er al. [17 made some modifications to the mean- trean- scheme for the challenging domain adaptation of natural image classification. Cai et al. [7] proposed multiple consistency regulations to solve cross-domain detection problem. Deng et al. [12) combined the idea of feature alignment and data augmentation based on mean-teacher scheme. These methods all assume single-source domain, and our knowledge, there is currently no work on multi-source teacher-student domain adaptation. Additionally, the mean-teacher approach does not sufficiently use the feedback signal from the target domain, so the performance improvement is limited.
3 Methodology
Problem statement. Suppose Df [Xt ME) .X3 where Y denotes label annotations for small amount of target domain samples X and X for target domain samples without any label annotations. The dimension of label vector is m. Ds, (Xi, Y where Yž denotes label annotations for i-th source domain samples X;, For the proposed semi-supervised multi-source-free domain adaptation problem, when the pretrained source classifiers Jr, is applied to the target domain, Given the source classifiers fr. for i i= L..... n and the target data Dr, the objective to find a target-domain mapping fs X + Y where Y denotes the predicted labels for target domain samples X
the source dataset Ds, is not accessible for i 1, 1....
Overview. As shown in 1ig.1, our framework is based on a First, multiple teacher models are pretrained according to each source domain, and then the student model is initialed using a randomly chosen teacher model. They are all composed of feature extractor (e.g., Resnet50 (2T) and multi-label classifier. The classifier consists of fully connected layer, where the input an one-dimensional expanded feature, and the output is the probability of each label. The objective function is the enror loss between the predicted output and the ground truth.
and one-student scheme.
Compared with traditional teacher-student models, our method is featured with two unique parts: (1) Coordinating weight learning; (2) Bilevel optimization. For the first part, a mapping is trained based on labeled target domain samples, which fuses the multi-teacher predictions adaptively for each target sample. This mapping will be used in the second part. In the initial iteration, the mapping and student model are trained based on labeled target samples. In the subsequent iterations, this part will only optimize the mapping while the student model will be updated by bilevel optimization. In the bilevel optimization part, the student and teacher models are updated alternately Specifically, for an unlabeled target sample, coordinating weight is generated, which provides optimization direction for each teacher model. Finally, these two parts will iteratively undated until convergence.
meta-learning manner.

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3.1 Coordinating Weight Learning
As mentioned earlier, the teacher models are trained on different source domain data. Due to different distributions, they often present different characteristics. Therefore, for target domain sample, the classification probability of each teacher model could be inconsistent. When we want to optimize a teacher model based on the target domain samples, the optimization direction of each teacher model should be different. So is necessary to obtain the contribution weight of each teacher model to the final classification results. We call this coordinating weight. Fortunately, we obtain the weight mapping with the labeled samples in the target domain.
As shown in Fig. (a), for obtaining the coordinating weight, we first input the labeled target sample of into the student network, and get the output B = gaaf) from feature extraction network g. where B E Rexhxw, with e, h, and w the number of channels, height, and width respectively. Then, we perform maximum pool operation on the feature map B to get th E Rixe which retains the most important information of each channel. Our mapping consists of two learnable variables μe and v, where μ E R=x1, y E Rexm. Then, we define mapping & jeby E R= for the target sample zf. After normalizing, we get the coordinating weight matrix where
caploj.k)
W J.A
(1)
==1 exp(
Suppose for the sample zf. the predictions of all teachers are formed as matrix P E R=xm. By taking the Hadamard product between the teacher predictions and the coordinating weight matrix, we obtain the fused prediction as the following,
Sum(P W)
where Sum(() means adding by rows. Denoting of = [s(1;; 0s) as the student prediction on the target sample of, we train the weight mapping and initialize student network using the following loss,
Lw = L (1 + aLKL (Vl. of) + B al) + ||/l))
(3)
where L (vi 31) = (o.slgg + (1 111.,,))ogll (o vi,))l represents the BCE (Binary Cross Entropy) loss, y1 is the ground truth, Og is the parameter of student network. LKL (ot of) divergence) loss which measures the
ExT Vi, (f /B,,) represents the KL (Kullback-Leibe distribution difference between the fused teacher prediction and student prediction. a and B are two balance parameters.
Remark.
The mapping generates coordinating weight with T. It not only reveals the complementarity of different teachers on different instances, but also, more interestingly, participates in the derivation of the update formula of teacher models in the bilevel optimization process (see Appendix), providing a reference for the update direction of different teachers.
3.2 Bilevel Optimization
The bilevel optimization problem 6 was first proposed in the field of game theory. It includes an upper-level optimization task and a lower-level optimization task, where the former contains the latter as constraint. Here, the upper-level optimization task (student) provides feedback signals to the lower-level optimization tasks (teachers) through the performance on labeled data and the coordinating weight mapping. For an unlabeled target sample arf, suppose the pseudo-label based on the learned coordinating weight mapping ø from multi-teachers (2) is yf, and the corresponding coordinating weight matrix is W., we can define a loss function I,, as follows,
T (O
0s) L(y'. Fi))
where J. = fs(tt As). fr, is the parameter of the i-th teacher network. Similarly, a loss function Ii (fI,...., Br., As) L (3t, 7() is defined for a labeled target samples x/. In the bilevel optimiza- tion task, updating 0s is the upper-level optimization task objective, while updating . , 0r, is the lower-level optimization task objective. The upper-level optimization task and the lower-level optimization task are mutually constrained. To reach the lower-level optimization task objective, the performance of the upper-level optimization task objective on the labeled target data is utilized
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as feedback signal. We formulate the objective function in lower-level optimization task as the following,
argmin 1 (0r).... Or,, P Dr Prn
s. 0 argmin Iu (0r1s'.. ,0,, 0s).
(5)
8g
Eq.45) cannot be optimized simply by gradient descent algorithm, because the teacher's parameters can not be updated until Os reaches the optimum. To overcome this issue, we resort to the idea of meta-learning [16 38 47 by making one-step approximation of the problem,
0 ~ 0s T7s Ve, (OT,, OT2 OT.,,0 0s)
(6)
where 7/s is the learning rate of the student network. Substituting Eq. (6) into Eq. new optimization objective function
5
we obtain
r2 (OT,," Or,, Os Tis Vo,Tu Tu (0r,, OT,,' ... ,rr.. 0s))
By optimizing Eq. 7 (see Appendix), we get the following update rules,
0' = 05 - 1]s Va,Tuu 1n Or: m " Vor, L (W)
(8)
for i = 1,... n. where 0'g and 0r the updated parameters corresponding to the student and teachers respectively. Vi = fT; 0r;)) ) W with W the ith-row coordinating weight vector of W, w.nt the i-th teacher. We obtain the pseudo label V. by binarizing Fy as: Vu. = when Fu. < 0.5 and Ve.j 1 for the other cases.
Additionally, in order to prevent optimizing teachers in the same direction, the predictions of the updated multiple teachers should be as far away from each other as possible. To that end, we define divergence loss as follows,
Cn -In Σ L2 (BT; (r.)) BTG (r,y)0r,)
(10)
j-1j
where By, (I.; .r:)) represents the max-pooled results of the output feature map of the i-th teacher network. Here, we apply a max-pooling operation to the output features of multiple teachers and calculate the distance with L2 norm. By requiring these feature maps to be far away each other, the optimization direction of teachers will be effectively adjusted. Finally, we update the i-th teacher network by the following rule,
02 = 02; "T:; (I) VogTaj" Vor, L (Tas Fi) + VVvr Co. CD)
(11)
where y is a hyperparameter.
Remark. Eq. reveals that the update direction of 0T. is determined by three factors: (1) Coordi- nating weight confuses the feedback signals from different teachers; (2) Student network parameters provide the feedback signals and generate coordinating weight; (3) Diversity constraint emphasizes the characteristic of different teacher networks. Interestingly, these three factors change over time during the meta- weta-nearning process. In addition to alternating updates of the student and teacher models, we also update the mapping periodically.
3.3 Optimization Process
The optimization process alternates between upper-level optimization and lower-level optimization. Since the coordinating weight mapping fixed the process bilevel optimization, with the progress of meta-learning, the coordinating weight W,, can not reflect the internal relationship between the source domains and the target domain gradually. Therefore, we use the learned teachers to update the mapping at regular intervals. The training process is summarized in Algorithm
4 Experiments
Datasets. Five publicly available chest x-ray datasets are used to construct our multi- adaptation scenarios. NIH-CXR14 (59] is large public dataset of chest x-ray, which contains 108,948

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Algorithm Our proposed MetaTeacher method.
Require: Student network parameters 5 teacher network parameters 710) T labeled data (r), unlabeled data (xt),, hyperparameters 9,7, 7, mapping updating interval T.
Ensure: Optimized student model S(N)
function METATEACHER _ Th , 3,3,7.7)
2:
((0), mapping Coordinating Weight Learning
3:
for t N - do
Upper-level optimization:
4:
Compute gradient V Vagco La
5:
Update the student: (ga11) t- ggm -15s V Lrx
> Eq.
Lower-level optimization:
6:
Compute gradient Va Vagu-1)
for all T ~ T do
7:
8:
Compute gradient Vg 700
C (Fu Vu)
9:
Compute gradient Vg
0000 CD
> Eq. (IO
10:
Update the i-th teacher: O 02f(441) f 0 01 1 I..
P Eq
([(V) Stett T.)". Vagi Tal= Vooee C (T + vVo,, Cp) end for
11:
if t%T = 0 then
13:
mapping < Coordinating Weight Learning
end if end for
15:
return S(N)
17: end function
front view x-ray images of 32,717 patients collected from NIH Clinical Center, with a total of 14 discase labels. MIMIC-CXR (23) contains 377,110 images and text reports, corresponding to 227,833 radiological studies conducted by Beth Israel Deaconess Medical Center Boston, Massachusetts. CheXpert 22 consists of 224,316 chest x-ray of 65,240 patients. The dataset collected chest x-ray examinations and related radiology reports performed at inpatient and outpatient centers at Stanford Hospital from October 2002 July 2017. Open-i [11] is collected by Indiana University Hospital through the network from open source literature and biomedical image collection. It contains 3955 radiology reports, corresponding
7470 frontal and lateral chest films. To be consistent with other datasets, we filter out the side chest x-ray in Open-I, leaving only 3955 frontal images. Google-Heallin CXR (3) is manually labeled by medical experts for CXR images with high accuracy and contains about 4000 images. We follow the traditional UDA setting, and choose the disease closed set in these five datasets multi classification labels, i.e., Atelectasis, Cardiomegaly, Effusion. Consolidation. Edema and Pneumonia. Four transfer scenarios are constructed, which are NIH-CXR14, CheXpert, MIMIC-CXC Open-i; -4RI4, CheXpert, MIMIC-CXR Google Health- CXRR; CheXpert, MIMIC-CX- CXR to NIH-CXR14 14 and NIH- IHH-X CheXpert to Open-i.
Implementation details. In order to make compromise between images in different datasets, scale the images to 128 before feeding them into the network. To expand the training set, several data augmentation techniques momentum of 0.9 is used as the optimizer. For the student model, the initial learning rate is 0.01 and the weight decay is 5c-4. The learning rate for coordinating weight mapping is 0.001; For the teacher models, the initial learning rate is 0.001 and the weight decay is 5e-6. The values of a, 3 and are as 0.5, 0.01 and 0.01 respectively. For the case when the target domains datasets are small-scale, CXR, we assume that there are 200 labeled data in the target
used, including random cropping and horizontal flipping. SGD with
such as Open-i and domains, and in order to give good initial condition for training, we randomly select a source model to initialize the target model. For the case when the target domains datasets are surge-scale, such as NIH-CXRI4, we assume that there are 500 labeled data in the target domains. Unless otherwise specified, the interval for updating coordinating weight mapping is set 100 iterations. Following the setting of multi-label medical image classification problems, the evaluation criterion is Area Under the Receiver Operating Characteristic (AUROC) 15 curve score.
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Table 1: Comparing the satie-o--hhe-ase methods on the transfer from CXR1C, CheXpert, MIMIC-XXR to
Open-i. Metric: AUROC.
Method
Atelectasis Cardiomegaly Effusion Consolidation Edema Pneumonia Average
DECISION
83,27
91.55
96.18
97.02
92.74
89.24
91.67
CAiDA 14
82.45
92.16
95.12
95.92
89.89
90.37
90.99
SHOT-best
81.48
91.22
94.19
95.10
88.96
89.58
90.09
MME 50
82,44
90.82
95.46
96.07
90.26
87.20
90.38
ECACI
82.60
92.18
96.32
95.97
90.70
89.61
91.23
Source Only(N)
83.09
87.20
96.11
95.10
86.87
77.40
87.63
Source Only(C)
82.26
87.64
94.71
96.61
90.22
75.12
87.76
Source Only(M)
80.63
91.31
94.87
94.53
84,91
82.78
88.05
(ane-aeue(average)
82.14
88.71
95.32
95.52
88.77
78.48
88.16
MetaTTeacher w/o mapping)
79.99
92.64
98.22
93.64
95,50
84.54
90.76
MetaTracchrrww/o update)
81.98
90.72
95.76
95,51
89,40
82.53
89.32
MetaTTcacher(all)
81.72
92.59
96.25
97.64
94.52
94.33
92.84
Table 2: Comparing the state-of of-the-art art methods on the transfer from NIH-CXR14, CheXpert, MIMIC-CK to Google-Health CXR. Metrie: AUROC.
Method
Atelectasis
Cardiomegaly Effusion Consolidation Edema Pneumonia Average
DECISION
77.24
81.71
85.94
79.03
83.48
83.68
81.85
CAiDA
14
76,90
81.82
87.55
79.62
85.10
82.72
82.29
SHOT-bes
35
75.43
80.28
86.63
77.88
82.37
81.22
80.64
MME
50
77.34
84.93
86.17
78.65
85.33
71.28
80.62
ECACL
30
76.27
84.54
87.00
79.95
85.82
72.66
81.05
Source Only(N)
76.54
84.48
86.36
75.66
83.94
62.59
78.26
Source Only(C)
72.09
76.45
84.55
79.07
68.25
58.39
73.13
Source Only(M)
68.04
79.38
84.17
72.41
68.71
52.60
70.88
(ine-tune(average)
73.48
80.14
85.96
74.17
74.74
60.20
74.78
MetaTeaccerr(wlo mapping)
75.62
83.91
85.40
80.27
75.13
81.77
80.35
Meta Teacherecee update)
76.75
84.30
86.67
78.59
82.31
65.84
79.08
MetaTeacherr(al))
77.65
79.52
88.73
78.74
86.73
84.78
82.69
4.1 Comparisons to State-of-the-Art
At present, there does not exist any experimental report on our problem setting. So we choose four category of methods for compare. The first category Source only which means directly applying teacher model to the target domain. The second category is Fine-tune Fine-aune(average) which fine-tune each teacher network using labeled target domain data, then average their predicted values. The third category is the state-of toitt--o--h--rrt multi- serti-souue--ffre ffurce-frre domain adaptation methods, which are DECISION [T) CAiDA [14, and SeOT-best. The SHOT-best refers adapting each souree domain separately through the SHOT (35) method. The model with the best performance the validation set selected. The final category is cunissupervised domain adaptation methods, including MME (50) and ECACI, (30). For the semi-supervised domain adaptation methods, we assume that the labeled target data are the same as our method. Since they are single-source based methods, we perform domain adaptation for each source model and take the best result.
Tables T show the comparison results on four transfer scenarios. MetaTeacher(all) our proposed method. Source Only(N), Source Only(C) and Source Only(M) are the teacher models respect to the NIH-CXRI4, CheXperr and MIMIC- CXR datasets respectively. For the scenario from CheXpert, MIMIC-CXP to NIH-CXR14, since the dataset NIH-CXRI contains 108,948 x-ray images, different from other scenarios, this time we do not need to initialize target model with the source models. It can be observed that our method achieves the best performance. The extensive experiments on four different transfer scenarios verify the adaptability of our method under multi-label chest x-ray dataset transfer cases. For the scenario from -XRI1, CheXpert to Open-i, as show in Table 4 the

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Table 3: Comparing the state-of- of-the-art methods on the transfer from CheXpert, MIMIC-CXP CXR to NIH- CXRI4.
Metric: AUROC.
Method
Atelectasis Cardiomegaly Effusion Consolidation Edema
Pneumonia Average
DECISION
72.99
80.73
79.37
75.52
82.30
71.38
77.05
CAiDA 14
72.64
81.12
80.25
74.73
81.02
70.44
76.70
SHOT- best 35
70.79
79.62
79.24
72.25
80.79
69.65
75.39
MME 50
72.90
81.73
81.01
73.11
81.03
71.52
76,88
ECACL 30
72.41
81.98
82.07
72.92
80.82
71.65
76.98
Source Only(N) Source Only(C)
72.31
80.52
79,42
69.66
77.95
67.37
74,54
70.45
79.66
79.98
68.26
78.01
70.82
73.86
Fine-tune(average)
71.52
80.29
80.08
68.97
78.02
69.05
74,66
Teacheeccer mapping)
72.05
81.58
78.36
72.94
82.19
69.82
76.16
Tetcherceer update)
72.24
80.69
79.56
69.80
78.13
70.55
75.16
Meachhaace)))ll
73.63
86.64
80.86
72.24
86.68
66.37
77.74
Table 4: Comparing the state-of-the-art methods the transfer from Open-i. Metrie: AUROC.
CheXpert to
Method
Atelectasis Cardiomegaly Effusion Consolidation Edema Pneumonia Average
DECISION
83.15
90.86
96.12
96.32
92.33
88.79
91.26
CAiDA
82.38
91.97
94.89
95.30
89.81
90.44
SHOT-bes
35
81.48
91.22
94.19
95.10
88.96
89.58
90.09
MME
81.46
90.40
94.86
97.73
89.79
87.31
90.26
ECACI. 30
82,22
88.76
96.04
96.85
92.43
87.90
90.70
Source Only(N)
83.09
87.20
96.11
95.10
86.87
77.40
87.63
Source Only(C)
82.26
87.64
94.71
96.61
90.22
75.12
87.76
Fine-tune(average)
82.66
87.98
95.85
95.67
88.58
77.02
87.96
MetaTeacherr mapping)
83.73
93.37
96.04
97.30
91.51
82.34
90.72
MetaTeacherr(woo update)
82.70
88.91
95.47
95.48
88.96
78.85
88.40
MetaTeacherraal)
82.11
92.42
96.80
97.07
92.20
91.27
91.98
performance of two source domains 0.86% lower than that of three source domains. Furthermore, MetaTeacher also has moderate training time and more clearer background (see Appendix).
4.2 Ablation Analysis and Discussion
Component analysis. In Tables 1-4, MetaTeacher(wWoo mapping) represents that our proposed method removes the part of coordinating weight learning and optimization substituted by average. Metaaeecherr(Wo update) means to remove the bilevel optimization process. In this situation, the weighted output of teachers is used to supervise the learning of student network. The results in the last three rows of Tables 1-4 show that these two parts are indispensable. It is worth mentioning that MetaTeachhrr mapping) still obtains promising performance due to the following reasons. First, for student updating, averaging predictions from multiple teachers is beneficial for student performance, consistent with the finding by [68]. Sccond, the fixed W is also involved the teacher optimization. It means bilevel optimization contributes more gain to the overall performance than the coordinating weight learning. However, the coordinating weight learning can judge which disease category the teacher is good at by weight, knowledge with different weights can be learned from different teachers. Therefore, the results each disease category are close to the predictions of the best teachers, such Pneumonia in Table and Atelectasis in Table 2 (also Appendix).
amount of labeled data in the target domain the transfer scenario of NIH-CXR14, CheXpert, MIMIC-CXR to Open-i. The experimental results show that the performance slowly improves as the amount of labeled data increases; small number of labeled target domain samples can achieve good results.
Effeets of proportion labeled target data. (ables]shows the influence

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Table 5: Effect of the size of labeled target data on the transfer from NIH-NNH-NE CXRI4, CheXpert, MIMIC-CXR to Open-i. Metric: AUROC.
Number(propotion)
Atelectasis
Cardiomegaly
Effusion
Consolidation
Edema
Pneumonia
Average
50(1.4%)
82.48
92.22
95.19
96.10
89.96
90.58
91.09
100(2.8%)
82.19
92.50
96.83
97.02
92.43
91.20
92.03
200(5.6%)
81.72
92.59
96.25
97.64
94.52
94.33
92.84
300(8.4%)
82.21
92.97
96.83
97.42
94.07
94.33
92.97
Sensitivity analysis of
Sensitivity analysis of [3
Sensitivity analysis of y
90
AUROCANN)
AUROC
Motaa Toache Basaline
47
valuo
value 76
value
0.0011
0005
(a.)
(b)
(C)
Figure 2: Effect of different hyperparameters on the transfer from NIHICXR14, CheXpert, MIMIC- CXR to Open-i. Baseline: source only(M).
Parameter analysis. We conduct parameter analysis experiment on the transfer scenario of NIH- CXR14, CheXpert, MIMIC- MCMMICCRRIRRI to Open-i. The basic strategy is to change parameter while other parameters are fixed. Our method MetaTeacher has three hyperparameters, i.e., a and / in Eq. (3), and y in (q.(1 1). Fig. (a) shows performance changing with the parameter When = 0, the coordinating weight mapping not trained effectively resulting the inability to determine the optimization direction of each teacher. When a gradually increases to around 0.5, the result achieve optimal performance. Fig. F (b) shows the influences of the parameters 3. When the 3 is too large, means that the coordinating weight learning part is ineffective and cannot express the relationship between the source domains. When 3 is set to 0, coordinating weight learning may overfit, which may cause coordinating weights work well on some instances but poorly on other instances; for this case, the performance 92.49% about 0.35% lower than the result 92.84% in Table [ Fig. (c)) shows the influences of the parameter ry on divergence loss. When is set to 0.01, the performance reaches the best, but with the continuous increase of y, the performance decreases obviously. When y 0, the result is 92.29%, which is 0.55% lower. We can also that our method is also quite stable for the parameters a, B and y in a large interval.
Conclusion
In this paper, we have proposed a novel framework, termed as MetaTeacher, for semi-supervised multi-source-free domain adaptation for medical image classification. The transfer learning process modeled as a multi-teacher and one-student scheme. We not only optimize the student, but also optimize the teachers through the student's feedback in the target domain. Our optimization is based on meta-learning with two main parts: coordinating weight learning. and bilevel optimization. The first part obtains the coordinating weight mapping which is then used to coordinate the teacher outputs and updates. Bilevel optimization updates the student based on the pseudo-labeled data produced by the teachers and updates each teacher based on the feedback signal generated by the student and other teachers. Extensive experiments on multi-label chest x-ray datasets empirically demonstrated the superiority of our method over many state-of-the-ar approaches.
Acknowledgments and Disclosure of Funding
This work was supported in part by the National Key R&D Program of China (2018YFE0203900), National Natural Science Foundation of China (62276048), Sichuan Science and Technology Program (2020YFG0476).
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15


Reviews:
[{"review_id": "ZixDxZLLhfT", "reviewer": "Reviewer_2Jhq", "summary": "This paper proposes a MetaTeacher for semi-supervised multi-source-free domain adaptation of medical image classification. The transfer learning process is modeled as a multi-teacher and one-student scheme. This model not only optimizes student, but also optimizes teachers through student’s feedback in the target domain. The optimization is based on meta-learning, which consists of two main part: coordinating weight learning, and bilevel optimization. Finally, experimental experiments on multi-label chest x-ray datasets empirically demonstrated the superiority of the proposed model against other SOTA methods.", "questions": "(1) Why do the authors design a multi-teacher one-student scheme? Whether we can set one-teacher and on-student framework? If yes, what is the performance when using one teacher?\n(2) In Table 1, it can be seen that the proposed model still obtains promising performance when without using coordinating weight learning. Please analyze the reasons for this case.\n(3) In Table, this experiment is only conducted on the proposed model, how does this is compared to other semi-supervised methods? \n\n", "limitations": "See questions.\n\n", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n(1) This paper studies a new problem setting, i.e., semi-supervised multi-source-free domain adaptation for multi-label medical image classification. \n(2) A mutual feedback mechanism is designed based on meta-learning between the target model and the source models for more coherent learning and adaptation.\n(3) The coordinating weight learning method is derived for dynamically revealing the performance differences of different source models over different classes.\n\nWeaknesses:\n(1) This is not clear why do the authors design a multi-teacher one-student scheme? Whether we can set one-teacher and on-student framework? If yes, what is the performance when using one teacher?\n(2) In Table 1, it can be seen that the proposed model still obtains promising performance when without using coordinating weight learning.\n(3) Table 5 shows the effects of the size of labeled target data on the transfer from NIH-CXR14, CheXpert, MIMIC-CXR to Open-i. However, this experiment is only conducted on the proposed model, how does this is compared to other semi-supervised methods? \n\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ["I don’t know"]}, "extra_text": {}}, {"review_id": "MXKag0Lf69V", "reviewer": "Reviewer_5gnh", "summary": "The paper presents a meta-learning approach for semi-supervised multi-domain source-free domain adaptation (SFDA). As first contribution, the authors extend the multi-domain SFDA problem to a semi-supervised learning scenario where a few labeled examples of the target domain are provided in addition to the unlabeled ones. As second contribution, they propose a meta-learning framework where a student learns from multiple teachers, each one trained on different source data. The student is trained to predict the correct class for labeled examples and, for unlabeled examples, to be consistent with a weighted average of the teachers' predictions where weights are learned. A bilevel optimization strategy is employed to update the parameters of the student and the teachers. The proposed method is evaluated on a multi-label chest X-ray classification task using five datasets. Results show that the method yield some improvements compared to recent approaches for SFDA.", "questions": "* Please provide some comparison in terms of computational complexity\n\n* Give an example or explain what the behavior of the method for predicting W.\n\n", "limitations": "The limitations of the method are not mentioned in the paper.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n* Novelty: Although it borrows from previous works like Meta Pseudo Labels, the overall method proposed in the paper is novel. In particular, the coordinating weight learning is most original. The extension of multi-source SFDA problem to a semi-supervised setting is also interesting, even if it relates to other machine learning problems like domain generalization and continual learning. \n\n* Experiments and results: The experimental evaluation of the method is comprehensive. It uses five large datasets and compares against several baselines, ablation variants and recent approaches. While the method's performance is only slightly better than competing approaches, it seems to provide better predictions when looking at the visualization examples.\n\nWeaknesses:\n\n* A potential weakness of the method is the high computational complexity brought by the bilevel optimization. It would be useful to compare the runtime of tested approaches, in addition to their accuracy.\n\n* Figure 2 indicates that varying beta and gamma does not improve the performance much and that the corresponding loss terms might not be so useful.\n\n* The coordinating weight learning is interesting, however experiments do not really study this component. If possible, it would be interesting to show what are weights for some examples of different sources.\n\n*  I feel that the related works in the Supplemental materials are more relevant than those in the main paper, as they focus on SFDA and compared methods of the experiments. I recommend the authors to add them to the main paper (or swap the content if lacking space).\n\nMinor comments:\n\n* Introduction: define multi-label classification.\n\n* Section 3.2: pesudo-label --> pseudo-label\n\n* Eq (5): min_{theta_S} --> argmin_{theta_S}  ?\n\n* Algorithm 1, line 5 (Sup.mat.): missing Theta_s on the right side ?\n\n  ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "J37mpqVRwIs", "reviewer": "Reviewer_6gX3", "summary": "1. The authors proposed a new problem setting, i.e., semi-supervised multi-source-free domain adaptation (SMDA) for multi-label medical image classification.\n\n2. A novel framework MetaTeacher based on a multi-teacher and one-student scheme is introduced to solve the proposed SMDA problem.\n\n3. A coordinating weight learning method is derived for dynamically revealing the performance differences of different source models over different classes. It is integrated with the semi-supervised bilevel optimization algorithm for consistently updating the teacher and student models.", "questions": "Please refer to the weakness part.", "limitations": "The authors have adequately addressed the limitations and potential negative societal impact of their work.", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength:\n\nThe proposed Semi-supervised Multi-source-free Domain Adaptation (SMDA) setting sounds interesting in the context of medical image classification.\n\nWeakness:\n\nMy major concern lies in the effectiveness of the proposed solution for SMDA.\n\nIn Tables 2-4, it is obvious that the proposed approach (ours (all)) only performs slightly better than DECISION [1] on the transfer to Google-Health-CXR, NIH-CXR14, and Open-i. Considering DECISION is the state-of-the-art multi-source-free domain adaptation method, I think the proposed solution cannot reflect well on the value of SMDA because the multi-source-free domain adaptation approach can already well tackle the SMDA setting.\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ["I don’t know"]}, "extra_text": {}}]

Author rebuttal:

Decision: Accept