Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.

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Improved OD and OC Fragmentation using Deep Learning for the Progression Recognition of Glaucoma

10.21203/rs.3.rs-490801/v1 ◽

2021 ◽

Author(s):

Shanmugam P ◽

Raja J ◽

Pitchai R

Keyword(s):

Deep Learning ◽

General Population ◽

Optic Disc ◽

State Of The Art ◽

Initial Stage ◽

Optic Cup ◽

Overall Performance ◽

Cup To Disc Ratio

Abstract Glaucoma is one of the most hazardous diseases, proceeding to impact and burden an extensive bit of our general population. Appropriately, the initial stage of identification of glaucoma is significant to prevent the permanent vision misfortune. The CDR is the important factor for glaucoma recognition. The precise fragmentation of optic disc and cup is yet an evolving issue. Most of the segmentation based glaucoma recognition methods depends on the handcrafted features. It affects the overall performance of the glaucoma recognition. To resolve this issue, an efficient deep learning based optic cup and disc segmentation using technique multi-label segmentation Au-net has been developed in this paper. The proposed method focusing on the optic cup-to-disc ratio for the recognition of glaucoma, which may be the best system for building a capable, energetic, and accurate structure for glaucoma analysis. This system has been simulated on DRISHTI datasets. The exploratory outcomes indicates the proposed strategy performs well to the best with state-of-the-art methodologies accomplishing a 99% of Accuracy, 88% of Sensitivity and 95.5% of Specificity on the DRISHTI GS1 dataset individually.

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Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

10.21203/rs.3.rs-56881/v3 ◽

2020 ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Semantic Segmentation ◽

Important Indicator ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider networkto improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 %on the REFUGE dataset, respectively.Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network

BMC Medical Imaging ◽

10.1186/s12880-020-00528-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Semantic Segmentation ◽

Important Indicator ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

Abstract Background Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7$$\%$$ % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79$$\%$$ % on the REFUGE dataset, respectively. Conclusions The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

10.21203/rs.3.rs-56881/v2 ◽

2020 ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Semantic Segmentation ◽

Important Indicator ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 % on the REFUGE dataset, respectively. Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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Full Convolutional Neural Network with Multi-Scale Residual Model for Optic Cup and Disc Segmentation

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3208 ◽

2020 ◽

Vol 10 (11) ◽

pp. 2733-2738

Author(s):

Yanxia Sun ◽

Peiqing ◽

Xiaoxu Geng ◽

Haiying Wang ◽

Jinke Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Cost Function ◽

Coordinate Transformation ◽

State Of The Art ◽

Fundus Images ◽

Early Screening ◽

Multi Scale ◽

Optic Cup ◽

Polar Coordinate

Accurate optic cup and optic disc (OC, OD) segmentation is the prerequisite for cup-disc ratio (CDR) calculation. In this paper, a new full convolutional neural network (FCN) with multi-scale residual module is proposed. Firstly, polar coordinate transformation was introduced to balance the CDR with space constraints, and CLAHE was implemented in fundus images for contrast enhancement. Secondly, W-Net-R model was proposed as the main framework, while the standard convolution unit was replaced by the multi-scale residual module. Finally, the multi-label cost function is utilized to guide its functioning. In the experiment, the REFUGE dataset was used for training, validation and testing. We obtained 0.979 and 0.904 for OD and OC segmentations on MIoU, which indicates a relative improvement of 4.04% and 3.55%, comparing with that of U-Net, respectively. Experiment results proved that our proposed method is superior to other state-of-the-art schemes on OC and OD segmentation, and could be a potential prospective tool for early screening of glaucoma.

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Repetitive Reprediction Deep Decipher for Semi-Supervised Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6082 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6170-6177

Author(s):

Guo-Hua Wang ◽

Jianxin Wu

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Large Scale ◽

State Of The Art ◽

Link Function ◽

Training Strategy ◽

Network Parameters ◽

Theoretical Support ◽

Percentage Points ◽

End To End

Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels. In this paper, we propose a principled end-to-end framework named deep decipher (D2) for SSL. Within the D2 framework, we prove that pseudo-labels are related to network predictions by an exponential link function, which gives a theoretical support for using predictions as pseudo-labels. Furthermore, we demonstrate that updating pseudo-labels by network predictions will make them uncertain. To mitigate this problem, we propose a training strategy called repetitive reprediction (R2). Finally, the proposed R2-D2 method is tested on the large-scale ImageNet dataset and outperforms state-of-the-art methods by 5 percentage points.

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Antimicrobial peptide identification using multi-scale convolutional network

BMC Bioinformatics ◽

10.1186/s12859-019-3327-y ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Xin Su ◽

Jing Xu ◽

Yanbin Yin ◽

Xiongwen Quan ◽

Han Zhang

Keyword(s):

Deep Learning ◽

Antimicrobial Peptide ◽

State Of The Art ◽

Peptide Identification ◽

The State ◽

Fusion Model ◽

Learning Methods ◽

Convolutional Network ◽

Multi Scale ◽

Peptide Database

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

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Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018441 ◽

2019 ◽

Vol 33 ◽

pp. 8441-8448 ◽

Cited By ~ 14

Author(s):

Tao Hu ◽

Pengwan Yang ◽

Chiliang Zhang ◽

Gang Yu ◽

Yadong Mu ◽

...

Keyword(s):

Deep Learning ◽

Feature Fusion ◽

State Of The Art ◽

Semantic Segmentation ◽

Research Topic ◽

Context Information ◽

Multi Scale ◽

Support Set ◽

Segmentation Task ◽

Context Features

Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learning, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% measured in mIoU in the 1-shot and 5-shot setting.

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Learning to Embed Sentences Using Attentive Recursive Trees

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016991 ◽

2019 ◽

Vol 33 ◽

pp. 6991-6998

Author(s):

Jiaxin Shi ◽

Lei Hou ◽

Juanzi Li ◽

Zhiyuan Liu ◽

Hanwang Zhang

Keyword(s):

Deep Learning ◽

State Of The Art ◽

The State ◽

Feature Representation ◽

Tree Structure ◽

Tree Model ◽

Training Strategy ◽

Recursive Tree ◽

Recursive Trees ◽

Embedding Methods

Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks to embed sentences with task-specific structures. However, existing models have no explicit mechanism to emphasize taskinformative words in the tree structure. To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task. Specifically, we construct the latent tree for a sentence in a proposed important-first strategy, and place more attentive words nearer to the root; thus, AR-Tree can inherently emphasize important words during the bottomup composition of the sentence embedding. We propose an end-to-end reinforced training strategy for AR-Tree, which is demonstrated to consistently outperform, or be at least comparable to, the state-of-the-art sentence embedding methods on three sentence understanding tasks.

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Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Multimedia Tools and Applications ◽

10.1007/s11042-020-10430-6 ◽

2021 ◽

Author(s):

Partha Sarathi Mangipudi ◽

Hari Mohan Pandey ◽

Ankur Choudhary

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Key Factors ◽

Glaucoma Diagnosis ◽

Proposed Model ◽

Effective System ◽

Benchmark Datasets ◽

Multiple Experts

AbstractGlaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation.

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