Multi-scale Attention for Diabetic Retinopathy Detection in Retinal Fundus Images
Authors: Temitope Ibrahim Amosa
Patrick Sebastian
Lila Iznita Izhar
Fatimat Adeola Adekola
Mardiyyah Adeola Salahudeen
Venue: Submitted to ICLR 2023 (2023)
Keywords: diabetes
deep learning
diabetic retinopathy
microvascular complication
hyperglycemia
attention
CNN

Abstract: The diagnosis and/or grading of diabetic retinopathy (DR) in the retina fundus has traditionally been done by physicians using manual procedures. However, there has been a significant demand for automated eye diagnostic and grading systems due to the constant rise in the number of persons with diabetes over the past few decades. An excellent diagnostic and predictive value for treatment planning exists with automatic DR grading based on retinal fundus pictures. With the majority of the current automated DR grading systems, it is exceedingly challenging to capture significant features because of the minor changes between severity levels. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. In order to increase the discriminative ability of the retrieved features, we implement a multi-scale attention mechanism within a deep convolutional neural network architecture in this research. Additionally, we provide a brand-new loss function termed modified grading loss that enhances the training convergence of the suggested strategy by taking into account the distance between various grades of distinct DR categories. The suggested technique is trained, validated, and tested using a dataset about diabetic retinopathy that is openly available. The experimental findings are presented to illustrate how well the suggested strategy competes.

Under review as a conference paper at ICLR 2023
MULTI-SCALE ATTENTION FOR DIABETIC RETINOPA- THY DETECTION IN RETINAL FUNDUS PHOTOGRAPHS
Anonymous authors Paper under double-blind review
ABSTRACT
The diagnosis and/or grading of diabetic retinopathy (DR) in the retina fundus has traditionally been done by physicians using manual procedures. However, there has been significant demand for automated eye diagnostic and grading systems due to the constant rise in the number of persons with diabetes over the past few decades. An excellent diagnostic and predictive value for treatment plan- ning exists with automatic DR grading based on retinal fundus pictures. With the majority of the current automated DR grading systems, it is exceedingly challeng- ing to capture significant features because the minor changes between severity levels. This paper presents deep learning-based method for automatically as- sessing diabetic retinopathy in retina fundus pictures. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. In order to increase the discriminative ability of the retrieved features, we implement multi-scale attention mechanism within deep convo- lutional neural network architecture in this research. Additionally, we provide a brand-new loss function termed modified grading loss that enhances the training convergence of the suggested strategy by taking into account the distance between various grades of distinct DR categories. The suggested technique trained, val- idated, and tested using dataset about diabetic retinopathy that is openly avail- able. The experimental findings are presented to illustrate how well the suggested strategy competes.
INTRODUCTION
Diabetic Retinopathy(DR) is a disorder caused by excessive blood sugar levels that damages the rear of the eye (retina). is a long-term microvascular issue brought on by uncontrolled diabetes mellitus (DM) and is one of the most significant consequences of type diabetes (T2DM) Ali et al. (2016); Naserrudin et al. (2022). Diabetic retinopathy is classified into four types: no visible di- abetic retinopathy (No DR), poonfroiieeeatiee diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and advanced diabetic eye disease (ADED). The multiple DR severity levels are broken down in Figure
Physicians frequently use the international clinical DR severity scale developed by the Ameri- can Academy of Ophthalmology (AAO) classify patients having non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), or maculopathy Ngah et al. (020)) defined NPDR as the presence of any of the following disorders with no signs of proliterative retinopathy: aneerr-see,ssms, intra retinal hemorrhage, venous beading, or intra retinal microvas- cular abnormalities (IRMAs). Neovascularization, vitreous or pre-retinal hemorrhage, or both, were used to describe PDR. Following that, fundus images were labeled as having no DR, NPDR, PDR, advanced diabetic retinopathy (ADED), cataract, maculopathy, or glaucoma suspicious Ngah et al. 2020); Mallika al. (201T). This classification assists in deciding when referral is necessary, how frequently to monitor/screen patients, how to treat them, and other factors Saxena et al. 2000))
Manual eye screening for DR entails identifying this eye disorder through visual examination of the fundus, either through direct inspection (in-person dilated eye examinations) or through analysis of number of studies (see e.g.. Liesen- feld et al. (2000); Olson et al. (2003); Gangaputra et al. (2013). fundus photography telemedicine has equivalent sensitivity and specificity as in-person screening for DR. Additionally, patients enjoy
digital color fundus photographs of the retina. According

---

Under review as a conference paper at ICLR 2023
No disease visible
Mild nonproliferative diabetic retinopathy
Moderate NPDR
Severe NPDR
PDR
NPOR haemorrhages),
Localized swelling of the small blood vessals the retina Imteroansuryeen
damaga to blood
vitreous/ hemorrhage or tractional retinal detachmmnt
(hard ssudates)
vessels tinteretinal
c clodure Icotion wool spots) of small blood vessols.
hamordhagge
verious Bei Beiiding.
Insranetioal
Figure 1: This is a figure. Schemes follow the same formatting, If there are multiple panels, they should be listed as: (a) Description of what is contained in the first panel. (b) Description of what is contained in the second panel. Figures should be placed in the main text near the first time they are cited. A caption on single line should be centered Ophthalytics (2022)
using it and find it to be less expensive. Manually analyzing this image is hard and time-consuming, and the problem gets worse in rural areas where access to skilled medical professionals is limited. Therefore, there is a great demand for screening programs that are more effective, repeatable, and comprehensive. This will eliminate access barriers, enable early diagnosis and treatment, and im- prove patient outcomes.
Numerous research studies have been conducted in over the years to detect DR automatically, with a focus on feature extraction and binary-class prediction [Baudoin et al. (984 Frame et al. (998);
Niemeijer al. (2009); Sinthanayyohhin et al. (2002); Niemeijer al. 2005 y Quellec et al. (008);
Abrâmoff et al. 2009 2013). These works have a few shortcomings but also some effectiveness.
First of all, since they employed hand-crafted features, the characteristics that are extracted from images are prone to noise, exposure, and artifacts, to name few. Second, feature placement and segmentation cannot be successfully included into the complete DR detection system. Additionally, just diagnosing to establish the presence or absence of DR rather than diagnosing the severity of DR could not adequately address real-world difficulties or assist physicians with their practice. Recently, deep learning techniques have found use in the processing of medical images, using Alyoubi et al. 2020 as an example.
In recent years, many studies on automatic DR grading/detection, including Gulshan et al. (2016):
Abràmoff et al. (2016); Li et al. (2019); Zhao et al. 2019); 9); Anoop et al. (2022); Bilal et al. (202 ));
Al-Antary & Arafa (2021) Zhao et al. (2020), utilized deep learning based based approaches, notably convolutional neural networks. These works make the best use of the autonomous feature extraction and excellent discriminability of deep learning (CNNs). In general, most of these studies utilized CNN for either binary classification Guulsaan et al. (2016): Anoop et al. (2022) or multi-class pre- diction Abràmoff et al.] (2016); [i al. (2019): Zhao et (2019); Bilal et al. (2021): AAl-Antary & Arafa (2021): Zhao et al. (2020). However, the approach ignores global information and loses crucial aspects Bello et al. (2019) due the down-sampling operators in CNN (i.e., convolution and pooling processes). Existing DR detection systems based on pure convolutional neural networks suffer from this loss of semantic information.
This paper suggests to incorporate attention mechanisms within deep convolutional neural ar- chitecture (Res-Net) for assessing diabetic retinopathy in retina fundus images. Motivated by the success of attention networks in machine translation Vaswani et al. (2017) and, more recently, com- puter vision tasks Li al. (2018), this work embeds multi-scale attention mechanisms within the layers of Res-Net56 architecture to improve classification accuracy for DR grading in fundus im- ages. In contrast to earlier studies, the attention mechanisms used this study are combined with robust CNN architecture for automated DR grading, with a particular emphasis on the capacity of attention networks to automatically learn to focus on salient features at various phases of the feature extraction. Furthermore, offer novel loss function, dubbed modified grading loss, that improves
2

---

Under review as a conference paper at ICLR 2023
the proposed method's training convergence by accounting for the distance between distinct grades of various DR categories. The following are the distinctive contributions of this work:
1. We propose a novel attention-based automatic DR detection framework, thereby removing the worry of laborious and time-consuming manual analysis.
2. To facilitate early DR detection, we approach the problem DR classification as a multi- class classification task.
3. To improves the proposed method's training convergence, we also propose a novel loss function, called modified grading loss.
2 BACKGROUND
2.1 DEEP RESIDUAL LEARNING
Naturally, deep networks incorporate many levels of features (low, mid, and high) and classifiers an end-to-end multi-layer manner. By adding more stacked layers (i.e., increasing the network depth), these levels of features be improved. However, networks become deeper, accuracy becomes saturated and quickly deteriorates. According to Simonyan & Zisserman (2014); He et al. (2016), increasing the number of layers in suitably deep model increases training error, proving that the problem training accuracy degradation not the result of "overfitting. To solve this issue of accuracy deterioration in "very deep" networks, He et al. presented the deep residual learning framework in He et al. (2016). In this method, the layers are allowed to fit residual mapping rather than expecting that every few stacked layers directly fit desired underlying mapping.
Technically, let consider G (x) as an underlying mapping to be fitted by a few stacked layers (rather than the entire layers the network), with x being the inputs to the first of these layers. Instead of expecting stacked layers to approach G (r), the approach expressly allow them to approximate
residual function, F := (a)) x. Thus, the initial function, G (ar) becomes (æ) + x. Despite the
fact that both forms should be able to approximate the goal functions asymptotically (as intended),
the ease learning may differ. As demonstrated in Figure 2 feedforward neural networks with
"shortcut connections" can be used to implement the formulation of F (x) + I.
Weight layer
F (x)
ReLU
Identity
Weight layer
F (x)
ReLU
Figure 2: This is figure. Schemes follow the same formatting.
Theoretically, a building block for deep residual learning defined in He et al. (016)) as Equa- tion In this case, the input and output vectors for the layers under consideration are x and respectively. The residual mapping to be learned is represented by the function F (x, (W.)). For the two-layer example in Fig. F W2 (Wix), where & stands for ReLU [29] and the biases are included for notations simplification. By using a shorteut connection and dlement-wise addition, the operation is carried out.
3

---

Under review as a conference paper at ICLR 2023
y= F(x, (W.)) -x
(I)
In Equation] [ the dimensions of x and F must be the same (1). In the event that this is the case (for instance, while switching the input/output channels), we can carry out a linear projection (refer He et al. (2016) for details). It is important to state that, there are no additional parameters or computational complexity introduced by the shortcut connections in Eqn. (I), In addition to being desirable in practice, this is significant in our comparisons of plain and residual networks. To further enhance the discriminative capacity of deep convolutional neural networks, several methods have been suggested, while its still remain an open research problem.
2.2 ATTENTION MODELS FOR DEEP NETWORKS
One effective method that has been extensively adopted in earlier work is representations (such as those from the very top layer of a CNN) that compress visual information in an image down to the prominent features. However, are lost due to series of convolution and down-sampling operations. Lower-level representation can aid in the preservation of this information. Working with these features, however, demands powerful method to direct the model to information relevant to the task at hand.
possible disadvantage is that important semantic information
The presence of attention one of the most perplexing aspects of the human visual system Xu itt al. (2015). Bahdanau et al. first suggest the artificial attention mechanism for neural machine translation in Bahdanau al. 2014). In recent years, attention models have excelled at a number of tasks involving computer vision and natural language processing [Bahdanau et al.) (2014); Xu
al. (2015); Chen al. (2016); Vaswani al. (2017): Dosovitskiy et al. (2020). Attention enables the model to concentrate image or sequence into a static representation Xu et al. (2015). This is especially helpful when an image has lot of distraction. Stochastic attention and deterministic attention are possible processes underlying the attention model. deterministic attention are often applied because they are differentiable, and often referred to "soft" attention.
the most salient features as needed rather than compressing entire
Additive attention and dot-product (multiplicative) attention are the two most often employed atten- tion functions. Formally, A query, set of key-value pairs, and output, all of which vectors, can be expressed as an attention function by mapping them to the corresponding output. The output is generated as weighted sum of the values, with the weight allocated each value determined by the query's compatibility function with the relevant key Vaswani et al. (017).
QK" T
Fass (Q,K, soflmarr
V
dg
In practice, the attention function, Fat is compute same time on a collection of queries pack into matrix Q. Also, the matrices K and V include both the keys and the values. In Egn we provide the mathematical expression for a dot-product attention as given in Vaswani et al. (017)..
3 METHOD
3.1 PROPOSED MULTI-SCALE ATTENTION NETWORK FOR DR GRADING (MAN-DR)
In this paper, we proposed the Multi-scale Attention Network for DR grading (MAN-DR) in fun- dus pictures. The "extremely deep" convolutional neural structure of the RAN-DR convolutional network employs a mixed attention approach. a baseline for this work, we adopt [Chnn et al. (016)) and the internal structure of the suggested RAN-DR is shown in Fig. 3 The development of the soft attention structure in our MAN-DR was motivated by the fact that there are only marginal differences across the different severity levels of DR and the need merge the multi-scale features from the CNNs.
A type of deep network layout referred to "share-net" Farabet al. 20012) Papandreou et al. (203)) that resize the input image to several scales and run each scale through separate deep network is employed. The final prediction is then computed based the fusion of the obtained
4

---

Under review as a conference paper at ICLR 2023
Image with scale 0.5
Deep CNN
Score Map
Attention Map
Prediction of DR Severity
Attention Network
Attention to Scale
Attention Map
Image
Deep CNN
Score Map
Figure 3: An illustration of the internal structure of the proposed network architecture Wang et al. 2017)
multi-scale features. To merge the multi-scale features for our proposed model, We suggest an attention model that learns weight the multi-scale features.
Assume an input image is resized to many scales E 1,.., S using share-net. Each scale is run through modified version of DeepLab Papandreou al. (2015) (Replacing the CNNs with Res- Net56, while the FCN weights are shared across all scales) to provide a score map for scale 8, indicated as fi, where spans all spatial coordinates (because is completely convolutional) and 1.... C' where C is the number of classes of interest. Bilinear interpolation is used to enlarge the score maps fi,c to the same resolution (with regard to the finest scale).
For all scales, we denote Ju k as weighted sum of score maps at (i, c):
Ju.o-
Σ w fū.
(3)
w can be expressed as given in Chen et al. (016) as:
exp(h
w
(4)
eap(h))
where he is the attention model's score map (i.c., the last layer output before SoftMax) at location for scale s. It's worth noting that wsi shared across all channels.
3.1.1 PROPOSED LOSS FUNCTION
As part of the optimization algorithm, the error of the current state of the model must be estimated repeatedly. However, traditional loss functions are only capable of breaking down multi-class classification into a number of binary classifications. In these typical loss functions, the distance between various classes is not taken into account. To address this problem "grading loss" func- tion was proposed by ZZho et al. (2019) which adds weights to the softmax function in order to decrease the loss-accuracy discrepancy and achieve improved convergence. This approach however adopt a distance- based weight function, which a weighted softmax. this work we intoduced normalization factor into the error function.
5

---

Under review as a conference paper at ICLR 2023
3.2 DATASET
We utilized publicly available datasets for Kaggle competition (Kaggle EyePACS) Kaggle (2015) for training, validation, and evaluation of the proposed algorithm. The Kaggle ByePACS datasets include 88,702 anonymized images of the retinal fundus obtained for diabetes screening. The dataset includes high-resolution retina images captured under various imaging conditions. For each subject, a left and right field is provided. In the dataset folder, containing train and test folders, images are identified by a subject id and are either labeled as left or right (for example, '15 jeft. the left eye of patient id 1). On scale of 0 to 4, a clinician rated the presence of diabetic retinopathy in each image. A .CsV is provided with the dataset, with two columns (image and level) for and image path location (image) and severity (level). For the severity level, the number simply implies: 0 No DR, Mild DR, 2 Moderate DR, 3 - Severe DR, and 4 Proliferative DR. Figure 2 (a) shows with data augmentation, while Figure 1 (b) shows the validation set. The training set contains 258 level 0 images, 2443 level images, 5292 level 2 images, 873 level 3 images, and 708 level 4 images. In Figure 3, provide the plot of the dataset distribution.
some samples of the training
Samples the Triin set
www.h
Samples the Valdittion set
and
Seyelty
Figure 4:
Samples from the employed dataset: (a) Some data instances from the training set (b) Some data instances from the validation set
3.2.1 DATA PREPROCESSING
The dataset available for this study was unbalanced, we however, chose to divide it into the follow- ing parts: 312 images per class make the 1560 images used for validation, while the 33566 images used for training (with 25496, 2132, 4980, 561, 397 images per class, corresponding classes 0 to 4). This split was made to ensure that both eyes of each subject in the database were in the same set. We performed data augmentation using rotations from to 360 degrees, zooming in and out from 0 to 20 pixels, and performing horizontal and vertical flips in order to balance the training set and increase the number of images training. All the training set's images underwent random augmentation, and for classes 2. 3, and 4, identical images underwent repeated augmentation with different augmentation parameters. 127480 images made up the resulting balanced training data set. In Figure 3 provide some samples of the training and validation set for this study.
4 EXPERIMENT
In this section, we conduct experiments to evaluate the accuracy and robustness of our MAN-DR. We compare model to the work of BIRA-NET [34] and SEA-NET [23], where attention mech-

---

Under review as a conference paper at ICLR 2023
anisms also employed. Ablation experiments used analyze the performance of different combinations among distinct sections in order to investigate the usefulness of various modules in the proposed MAN-DR.
Tensorflow and the Keras API are used to implement the proposed model. The training accelerated by single NVIDIA Tesla P100 GPU with 16GB VRAM, and the model was trained using stochastic gradient descent (SGD) optimizer with a momentum of 0.9 and batch size of 32. The 12 regularization applied to weights with weight decay factor of Se-7 and an initial learning rate of 0.05. In Fig. we provide the plot of epoch against accurracy for both training and validation.
Implementation detail:
Training and validation accuracy
0.9
0.8
0.7
0.6
Training accuracy
Validation accuracy
0.5
100
200
300
400
500
Figure 5: Plot of training and validation accuracy
Performance measure: We construct the confusion matrix, in which the number of predictions in each class are shown, measure performance proportion of the diagonal elements, which reflect the number of points for which the predicted label equal to the true label, can be used to determine the average of classification accuracy (ACA) based on the confusion matrix. Receiver operating characteristics (ROC) Hajian Tilaki (2013), which reflect the true positive rate (TPR) and the false positive rate (FPR) different threshold settings, are calculated to assess the diagnostic performance the suggested approach. AUC (Area Under Curve), which is based ROC, is calculated for comparison.
the multi-class classification problem. The
Figure 6 depicts the confusion matrix for the proposed MAN-DR, with the horizontal axis repre- senting the anticipated classes and the vertical axis representing the genuine classes. This confusion matrix is not normalize in other to reveal the true values of correctly classify and wrongly classify instances.
4.1 DISCUSSION
Table displays the experimental outcomes of all methods on the testing data. Even without the proposed loss function, the suggested framework outperforms competing techniques in AUC. The suggest approach shown great deal of improvements as to when the share-net architecture is em- ployed with attention. Using the proposed error function, model convergence rate increases. Except for class 3 gnd 4, which is generally classified into class 0,1, or 2, each class in the confusion matrix most likely to forecasted into the correct class. is obvious that class 3, 4 the most difficult to distinguish, whereas normal (class 0) is the easiest.

---

Under review as a conference paper at ICLR 2023
3417
2253
750
190
124
378
336
21
9
2
714
482
244
79
54
97
62
27
66
21
27
0
1
2
4
Figure 6: Classification Confusion matrix for the proposed MAN DR
1.0
Model Prediction (AUC: 0.57)
Random Guessing
0.8
True Positive Rate
0.6
0.4
0.2
0.0
False Positive Rate
Figure 7: Receiver operating characteristies computed for the proposed model
5 CONCLUSION
The need for regular eye screening for diabetes patients has proven to be an effective measure to prevent DR-related vision impairments. Owing to sustained increment in number of individuals living with diabetes globally, there have been high demanded for automated DR grading. This research presents multi-scale attention-based deep learning architecture for diabetic retinopathy grading, with share-net structure implemented for learning multi-scale high discrimantive feature is applied. Furthermore, the proposed grading loss function contributes to the proposed approach's substantially improved convergence. The ablation analyses reveal that the proposed components significantly increase classification performance. The suggested MAN-DR competitive with state- of-the-art approaches, demonstrated by the experimental findings.
8

---

Under review as a conference paper at ICLR 2023
ACKNOWLEDGMENTS
This work supported by the YUTP Fundamental Research Grant.
REFERENCES
Michael D Abråmoff, Bram Van Ginneken, and Meindert Niemeijer. Automatic detection of red lesions in digital color fundus photographs, January 6 2009. US Patent 7,474,775.
Michael D Abråmoff, James C Folk, Dennis P Han, Jonathan D Walker, David F Williams, Stephen R Russell, Pascale Massin, Beatrice Cochener, Philippe Gain, Tang, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA ophthalmology, 131(3)3351-377, 2013.
Michael David Abrämoff, Yiyue Lou, Ali Erginay, Warren Clarida, Ryan Amelon, James C Folk, and Meindert Niemeijer. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative ophthalmology & visual sci- ence, (7(11))5200-5222 2016.
Mohammad T Al-Antary and Yasmine Arafa. Multi-scale attention network for diabetic retinopathy classification. IEEE Access, 9:54190-54200, 2021.
Masliza Mohd Ali, Nani Draman, Wan MIW Mohamed, Azhany Yaakub, and Zunaina Embong- Predictors of proliferative diabetic retinopathy among patients with type 2 diabetes mellitus in malaysia as detected by fundus photography. Journal of Taibah University Medical Sciences, 11 (4):353-358, 2016.
Wejdan L Alyoubi, Wafaa M Shalash, and Maysoon Abulkhair. Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked, 20:100377, 2020.
BK Anoop al. Binary classification of dr-diabetic retinopathy using with fundus colour images. Materials Today: Proceedings, 58:212-216, 2022.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.044 2014.
Claude E Baudoin, Bruno J Lay, and Jean-Claude Klein. Automatic detection of microaneurysms in diabetic fluorescein angiography. Revue d' épidémiolooii et de santé publique, 32(3-4):254-261, 1984.
Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoe V Le.. Attention augmented convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision, pp. +286-3295, 2019.
Anas Bilal, Guangmin Sun, Yu Li, Sarah Mazhar, and Abdul Qadir Khan. Diabetic retinopathy detection and classification using mixed models for disease grading database. IEEE Access, 9: 23544-23553, 2021.
Liang-Chich Chen, Yi Yang, Jiang Wang. Wei Xu, and Alan Yuille. Attention scale: Scale- aware semantic image segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. (649. 2016.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 2020.
Clement Farabet, Camille Couprie, Laurent Najman, and Yann LeCun. Learning hierarchical fea- tures for scene labeling. IEEE transactions on pattern analysis and machine intelligence, 35(8): 1915-1929, 2012.

---

Under review as a conference paper at ICLR 2023
Allan J Frame, Peter E Undrill, Michael Cree, John A Olson, Kenneth C McHardy, Peter F Sharp, and John V Forrester. A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Computers in biology and medicine, 28(3):225-233 1998.
Sapna Gangaputra, James Lovato, Larry Hubbard, Matthew D Davis, Barbara A Esser, Walter T Ambrosius, Emily Y Chew. Craig Greven, Letitia H Perdue, Wai T Wong, et al. Comparison of standardized clinical classification with fundus photograph grading for the assessment of diabetic retinopathy and diabetic macular edema severity. Retina (Philadelphia, Pa.), 33(7), 2013. 2013.
Varun Gulshan, Lily Peng, Mare Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22):2402 24122244000 2016.
Karimollah Hajian-Tilaki Receiver operating characteristic (roc) curve analysis for medical diag- nostic test evaluation. Caspian journal of internal medicine, 4(2):627, 2013.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp- 770-778, 2016.
Kaggle. Diabetic retinopathy, 2015.
URL
https: wwwp https::/www.kaggle...o
dibbetic-retihopattyy-eeCe
Tao Li, Yingqi Gao, Kai Wang, Song Guo, Hanruo Liu, and Hong Kang. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501:511-522, 2019.
Wei Li. Xiatian Zhu, and Shaogang Gong. Harmonious attention network for person re- identification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2285 2295-2294, 2018
Bernd Liesenfeld, Eva Kohner, WOLFGANG Pichlmeier, Stefan Kluthe, Steve Aldington, Massimo Porta, Toke Bek, Marco Obermaier, Hans Mayer, Gerd Mann, et al. A telemedical approach to the screening of diabetic retinopathy: digital fundus photography. Diabetes care, 23(3):345-33 2000.
PS Mallika, PY Lee, WL Cheah, JS Wong, SAR Syed Alwi, H Nor Hayati, and AK Tan. Risk factors for diabetic retinopathy in diabetics screened using fundus photography at primary health care setting in east malaysia. Malaysian family physician: the official journal of the Academy of Family Physicians of Malaysia, 6(2-3):60, 2011.
Nurul Athirah Naserrudin, Mohammad Saffree Jeffree, Nirmal Kaur, Syed Sharizman Syed Ab- dul Rahim, and Mohd Yusof Ibrahim. Diabetic retinopathy among type 2 diabetes mellitus patients in sabah primary health aldnicsaddressing the underlying factors. Plos one, 17(1): c0261249, 2022.
Nor Fariza Ngah, Nor Asiah Muhamad, Zalifa Zakiah Asnir, Roslin Azni Abdul Aziz, Zaidah Mhad Kassim, Siti Aishah Sahar, Nor Azita Ahmad Tarmidzi, Li Yen Chan, Rosmawati Uthman, Narjis Satar, et al. Descriptive assessment on diabetic retinopathy screening in an awareness programme in malaysia. International journal of ophthalmology, 11111:8808 2020.
Meindert Niemeijer, Bram Van Ginneken, Joes Staal, Maria SA Suttorp-Schulten and Michael D Abràmoff. Automatic detection of red lesions digital color fundus photographs. IEEE Trans- actions medical imaging, 24(5):584-59) 2005.
Meindert Niemeijer, Bram Van Ginneken, Michael J Cree, Atsushi Mizutani, Gwénolé Quellec, Clara Sánchez, Bob Zhang, Roberto Hornero, Mathieu Lamard, Chisako Muramatsu, et al. Retinopathy online challenge: automatic detection microaneurysms in digital color fundus photographs. IEFE transactions medical imaging, 29(1 29(1):185-195, 2009.
10

---

Under review as a conference paper at ICLR 2023
JA Olson, FM Strachan, JH Hipwell, Keith A Goatman, KC McHardy, John Vincent Forrester, and Peter Frederick Sharp. A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy. Diabetic medicine, 20(7):528-534, 2003.
Ophthalytics. Diabetic retinopathy, 2022. URL
ou--teednollrryalaaett------ htw.:eeeiicc..eeeotttatt..a..
George Papandreou, Liang- Chieh Chen, Kevin P Murphy, and Alan L Yuille. Weakly-and semi- supervised learning of deep convolutional network for semantic image segmentation. In Pro- ceedings of the IEEE international conference on computer vision, pp. 1742-1750, 2015.
Gwénolé Quellec, Mathieu Lamard, Pierre Marie Josselin, Guy Cazuguel, Béatrice Cochener, and Christian Roux. Optimal wavelet transform for the detection of microaneurysms in retina pho- tographs. IEEE transactions on medical imaging, (7(9))1230-122 2008.
Gaurav Saxena, Dhirendra Kumar Verma, Amit Paraye, Alpana Rajan, and Anil Rawat. Improved and robust deep learning agent for preliminary detection of diabetic retinopathy using public datasets. Intelligence-Bae Based Medicine, 3: 3:100022, 2020.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint 1w0iiv:100..5555 2014.
Chanjira Sinthanayothin, James F Boyce, Tom H Williamson, Helen I. Cook, Evelyn Mensah, Shan- tanu Lal, and David Usher. Automated detection of diabetic retinopathy digital fundus images. Diabetic medicine, 19(2)1105-112, 2002.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, tukasz Kaiser, and Illia Polosukhin. Attention you need. Advances in neural informa- tion processing systems, 30, 2017.
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang. Residual attention network for image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3156-3164, 2017
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning, pp. 2048-2057. PMLR, 2015.
Ziyuan Zhao, Kerui Zhang, Xuejie Hao, Jing Tian, Matthew Chin Heng Chua, Chen, and Xin Xu. Bira-net: Bilinear attention net for diabetic retinopathy grading. In 2019 IEEE International Conference on Image Processing (ICIP), pp. 1385-1389. IEEE, 2019.
Ziyuan Zhao, Kartik Chopra, Zeng Zeng, and Xiaoli Li. Sea-net: Squeeze-and-exciiae attention
net for diabetic retinopathy grading. In 2020 IEEE International Conference on Image Processing (ICIP), pp. 2496-2500. IEEE, 2020.
A APPENDIX
You may include other additional sections here.
11


Reviews:
[{"review_id": "UGDJjv5Br5G", "reviewer": "Reviewer_mS2T", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "This work develops a deep network for assessing diabetic retinopathy (DR) in retina fundus images by implementing a multi-scale attention mechanism, and a brand-new loss function.\nExperimental results show that the effectiveness of the developed method.\n", "summary_of_the_review": "1.\tThe technical novelties of this work are not enough for publication in ICLR. \n2.\tThe experiments are not convinced. The authors do not provide comparisons against state-of-the-art methods and any ablation study experiments.\n3. \tWriting issues: The sentence “This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures.” appears two times in the Abstract.", "strength_and_weaknesses": "Strengths:\n1.\tThis work develops a CNN based on a multi-scale attention for DR grading.\n2.\tThe experimental results verify the effectiveness of the developed method.\nWeaknesses:\n1.\tThis work has limited technical novelties. The multiscale attention strategy is to simply learn attention maps at different scales.  Such technical novelties are not enough for publication in ICLR\n2.\tThe experimental results are not convinced. It seems that the authors only report the results of the developed method. It neglects the comparisons with state-of-the-art methods.\n3.\tIn the experiments, the authors do not provide any ablation study experiments.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "HCtuRnXOWb", "reviewer": "Reviewer_zeSz", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "The work proposes to combine a multi-scale attention mechanism with a deep convolutional neural network to improve discriminative abilities of the features used for diabetic retinopathy grading. The modified training loss is used for improving model convergence by comparing various grades of the disorder. The experiments are performed on the publicly available dataset to show advantages of the method over other attention-based techniques.  ", "summary_of_the_review": "The presented paper looks incomplete. Some additional work should be done to improve its quality, provide more detailed analysis of results and justify the novelty of the method.", "strength_and_weaknesses": "The paper addresses an important problem of diabetic retinopathy (DR) grading, what's important in encouraging further improvements and automation of medical procedures. The background of the problem is clearly explained along with existing methods used for the prediction of the DR disorder. \n\nHowever, the work suffers from some significant omissions that affect its quality:\n1) As specified in the experiments section, other techniques for DR grading using attention mechanisms have been proposed. It's not clear how the method differs from them. \n2) Comparison of results is missing - there is no Table 1 that is referred to in the text.\n3) Results are not satisfactory, the performance of the model is quite poor, what hasn't been discussed. A details explanation of results, ideas for improvements and future work would be beneficial.\n4) It's mentioned that the used dataset was imbalanced, what can be also seen in the presented confusion matrix. Majority of samples were classified as category 0. Have you tried any techniques to address this issue, such as reweighing/over/undersampling, etc.?\n5) Weighted softmax has been used in other studies before. How exactly does the new loss function differ from that? It's not clear.\n6) Selection of specific neural networks hasn't been explained. It would be interesting to compare different topologies. \n7) Background section contains explanation of well known techniques. It's sufficient to refer to them instead of introducing them in details. Similarly, attention mechanism is known, so there is no need for such detailed introduction of this technique.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "51JI51AX-v1", "reviewer": "Reviewer_5o7H", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "The paper presents a deep-learning method for the automatic assessment of diabetic retinopathy in retina fundus pictures. A multi-scale attention mechanism is developed to enhance the discriminative power of the model. In contrast to the previous methods, the attention mechanisms used in this work are combined with the network architecture to automatically learn to focus on salient features at various phases of the feature extraction. Furthermore, a novel loss function, named modified grading loss, is proposed to improve the training convergence by accounting for the distance between distinct grades\nof various DR categories. \n", "summary_of_the_review": "The paper combines several ideas from previous works including a multi-scale attention mechanism and grading loss function to improve the classification of DR in retina images. The methodology lacks novelty and the problem itself is not challenging enough. Furthermore, the experimental setup is simple and does not present enough evaluations and ablation studies to validate the claims in the paper including the necessity of soft or multi-scale attention and the grading loss. ", "strength_and_weaknesses": "Strength: \n-The paper is well-written and easy to understand. \n-Developing applications of deep learning in other domains especially healthcare is highly important and requires major attention from the community. \n\n\n\n\nWeaknesses:\nMAJOR:\n-A major shortcoming of the paper is the lack of novelty. The attention mechanism, network architectures, the idea of passing different scales of the same input to different networks, and the grading loss function are cited from previous works and the current work seems as an adaptation of a previously developed model for the special tasks of DR classification. Further, the task itself is also a simple classification problem that does not incur novel challenges. A very similar paper to the current work is also published: “Al-Antary, Mohammad T., and Yasmine Arafa. \"Multi-scale attention network for diabetic retinopathy classification.\" IEEE Access 9 (2021): 54190-54200.”. \n-The reasoning for developing soft attention is vague and requires further clarification. Perhaps, conducting a relevant experiment can be beneficial to clarify its usefulness.  \n-The description of the grading loss function is vague and requires a formal formulation to clarify the concept. \n-Only a single dataset is used for the evaluation. This limits the comprehensiveness of the validations. \n-How is the ROC curve for the 4-class classification problem computed? \n\n\nMINOR: \n\n-“(DR)” in the first line of the introduction requires a space. \n-“This is a figure” in Figure 2 is redundant. \n-Section 3.1.1: “this approach adopt” -> “this approach adopts”\n-Section 3.1.1: “In this work we intoduced” requires a comma after work and also there is a typo in introduced. \n-Section 4.1: “the suggest approach” -> “the suggested approach”\n-Section 4.1: “shown” -> “shows”\n-Section 4.1: “class 3 qnd 4” -> “class 3 and 4”\n-Section 4.1: “class 3, 4” -> “classes 3 and 4”\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject