A1Cnet: Deep Learning for Glycated Hemoglobin Estimation∗

Xi Peng1, Jiawei Du2, Joey Tianyi Zhou2

1College of Computer Science, Sichuan University, China,2Institute of High Performance Computing, A*STAR, Singapore.

1pengx.gm@gmail.com 2{zhouty,dujw}@ihpc.a-star.edu.sg.

Abstract

Glycated hemoglobin or called A1C could effec-tively diagnose diabetes, which however has suf-fered from a variety of challenges because the mea-surement of Glycated hemoglobin via blood testis painful and expensive. Although deep learningmethods for analyzing glycated hemoglobin fromfundus photographs proves to be effective in re-cent, their performance is still unsatisfactory due tothe low resolution of small blood vessels in retinaand extremely imbalanced training data. To addressthese challenges, we propose a novel deep learn-ing network named A1Cnet, a specialized networkfor glycated Hemoglobin estimation. The pro-posed A1Cnet consists of dilated convolution lay-ers and group convolution layers which simultane-ously capture the detailed patterns and alleviate thesample bias. Experiments demonstrate the superi-ority of proposed model over other state-of-the-artmethods on the estimation of Glycated hemoglobinonly with fundus photographs.

1 IntroductionHigh blood glucose level is a determined factor for diabetesdiagnosis [Poplin et al., 2018]. However, since blood glu-cose level always fluctuates along time, hemoglobin (A1C)leads to a more practical diagnostic test for diabetes since itcould reflect three-month average blood glucose level. Bothblood glucose level and Glycated Hemoglobin are measuredvia blood test, which is intrusive, painful and expensive fordiabetes screening.

On the other hand, a lot of clinical studies demonstratethat blood vessels lesions in fundus photo is one of thekey diabetes complications [Ting et al., 2017; Poplin et al.,2018]. With the advance of deep learning methods in im-age recognition [Szegedy et al., 2015], fundus photogra-phy turns out be a cheaper and non-intrusive alternative bydirectly predicting A1C level from fundus photos. Unfor-tunately, most existing deep learning methods fail to at-tain clinically acceptable performance [Ting et al., 2017;

∗Corresponding Author: Joey Tianyi Zhou.

Poplin et al., 2018]. For example, [Poplin et al., 2018] pro-posed the-very-first deep network to estimate A1C level fromfundus photos. Comparing with other tasks such as classifica-tion of gender and estimation of age from fundus photos, theperformance of A1C estimation is remarkably inferior and farbehind the human expertise. By analyzing the data, we foundthat the possible reasons leading such inferior performanceare twofold, i.e., the low resolution of small blood vesselsin retina (Figure 3) and extremely imbalanced training data(Figure 4).

Figure 1: Normal fundus Figure 2: Abnormal fundus

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Figure 3: Small blood vessels

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Figure 4: Data distributionTo address these two problems, we propose a novel net-

work that is able to capture the detailed blood vessels thanksto two observations. First, B1C level is reflected by the wholeblood vessels lesions in retina. As shown in Figure 1 and2, small blood vessels are thinner in diabetic patients thanblood vessels in healthy person. Second, eye positions of dif-ferent individuals significantly vary in fundus photography.Thus, we intend to dispense fully connected layers and useless max-pooling layers instead. Fully connected layers indeep learning networks make the network sensitive to the ab-solute location of feature patterns. In contrast, convolution

layers compute feature patterns in different locations of animage with the same kernel. To simplify the network archi-tecture, we employ down-sampling layers to discard less im-portant features in the network. Although Max-pooling is themost widely used down-sampling method, it usually leadsto the loss of resolution. Comparing with the classificationtask, the resolution problem will affect the regression per-formance more significantly due to more fine-grained scalesconsidered. Therefore, we largely reduce the number of max-pooling layers and replace them with dilated convolution lay-ers that demonstrate its ability to preserve the high resolution[Hamaguchi et al., 2018].

Apart from the low resolution problem, extremely imbal-anced data distribution in training dataset also hinders theoptimization of networks. As shown in Figure 4, negativesamples are dominating in medical datasets. To alleviate theaforementioned sample bias, we propose to group the trainingdata into multiple clusters based on the A1C level.

2 Related WorkIn recent, some neural networks have been applied in diag-nosing fundus photography [Poplin et al., 2018; Ting et al.,2017; Simonyan and Zisserman, 2014]. Although some ofthem achieve promising performance, they did not give com-parable results in predicting A1C level than predicting otherrisk factors. More recently, [Hamaguchi et al., 2018] provedthe effectiveness of dilated convolutions on small object seg-mentation. The dilated convolutions can preserve the detailedbasic feature patterns without losing resolutions. However,the works have still faced the sample bias issue.

3 MethodArchitecture and Loss Function To guarantee the conver-gence of deep learning, we use the first six layers of the well-trained VGG16 [Simonyan and Zisserman, 2014] networkwith following modifications. The third max-pooling layer isreserved and the sixth max-pooling layer was removed. Thestride of the fifth convolution layer is set to two to compen-sate the removed max-pooling layer. Two dilated convolutionlayer with a dilation of two follow the above five layers. Theyboth have 256 filters with a kernel size of three. While thestride of them are set to one and two, respectively. A globalaverage pooling layer generates the output of the network.We take Mean Squared Error (MSE) loss for training.

Uniform Sampling Since there is no fully connected lay-ers in the network, the size of inputs can be set to any values.We resize the input images to 224 × 224 × 3. Due to theextremely imbalance of training dataset, we manually con-trol the data distribution of each batch to be uniform. Thetraining dataset is divided into several groups according tothe A1C level. A fixed number of training samples are ran-domly selected among each group to form the mini-batch.As a result, the samples will follow the uniform distribu-tion in each epoch. Specifically, the samples in sparse groupwill be selected more frequently than the samples in densegroup. Therefore, samples with abnormal A1C level con-tributes more on the backpropagation of the network.

4 ExperimentsWe train our proposed A1Cnet with 20,425 macula-centeredtraining samples and evaluate it using other 5,147 macula-centered samples. A momentum SGD optimizer with 0.0001learning rate and β = 0.9 is used to train A1Cnet for 150epochs. The first 15 layers are loaded from the pretrainedV GG16 [Simonyan and Zisserman, 2014] weights, the otherlayers are randomly initialized by N (0, 1).

We compare our proposed A1Cnet with the state-of-the-art baseline [Poplin et al., 2018] and [Ting et al., 2017]. Byfollowing the protocol in [Poplin et al., 2018], we use MeanAbsolute Error (MAE) and coefficient of determination r2 asthe evaluation metrics. Experiments results are presented inTable 1 and the state-of-the-arts results of A1C level estima-tion are achieved.

Table 1: Results of A1C Level Estimation.Method MAE r2

[Ting et al., 2017] 1.880 n.a[Poplin et al., 2018] 1.390 0.090Ours 0.775 0.303

5 ConclusionIn this paper, we analyze the regression of GlycatedHemoglobin(A1C) with deep neural networks and proposea specialized design network to enhance the performance inregression of Glycated Hemoglobin (A1C). The experimentsresults verify the effectiveness of the proposed network.

References[Hamaguchi et al., 2018] Ryuhei Hamaguchi, Aito Fujita,

Keisuke Nemoto, and et al. Effective use of dilated con-volutions for segmenting small object instances in remotesensing imagery. In IEEE Winter Conf Applications ofComputer Vis, pages 1442–1450. IEEE, 2018.

[Poplin et al., 2018] Ryan Poplin, Avinash V Varadarajan,Katy Blumer, and et al. Prediction of cardiovascular riskfactors from retinal fundus photographs via deep learning.Nature Biomedical Engineering, 2(3):158, 2018.

[Simonyan and Zisserman, 2014] Karen Simonyan and An-drew Zisserman. Very deep convolutional networksfor large-scale image recognition. arXiv preprintarXiv:1409.1556, 2014.

[Szegedy et al., 2015] Christian Szegedy, Wei Liu, YangqingJia, and et al. Going deeper with convolutions. In Proc.of IEEE Conf Comput Vis and Pattern recogni, pages 1–9,2015.

[Ting et al., 2017] Daniel Shu Wei Ting, Carol Yim-LuiCheung, Gilbert Lim, and et al. Development and valida-tion of a deep learning system for diabetic retinopathy andrelated eye diseases using retinal images from multieth-nic populations with diabetes. Jama, 318(22):2211–2223,2017.