Mass classification of benign and malignant with a new twin support vector machine joint 
                
                  
                
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Macular edema is the accumulation of fluid leakage from retinal capillaries.Optical coherence tomography (OCT) images can show changes in the retinal tissue caused by ophthalmological diseases, such as fluid accumulation. Therefore, the segmentation of retinal fluid is important. Some methods based on image processing and machine learning often require large amounts of labeled data and rich domain knowledge. This study proposes a self-ensembling semisupervised model based on uncertainty guidance, namely, UGNet. The model is trained end-to-end with a few labeled data and plenty of unlabeled data, and contains a teacher model and a student model with the same architecture. The two models consist of an encoder and three decoders, which are used to predict the probability map, contour map, and distance map. The segmentation result is the fusion result of the three maps generated by the student model. The selective kernel module (SKM) is embedded in the decoder to make the model adaptively adjust the receptive field according to the size of the fluid. The uncertainty of teacher model evaluation guides the student model to learn more reliable knowledge. The proposed method is trained and evaluated on the RETOUCH challenge dataset. The experimental results show that our method achieves better segmentation results than other start-of-the-art methods.

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Uncertainty‐guided self‐ensembling model for semi‐supervised segmentation of multiclass retinal fluid in optical coherence tomography images

Liu

Wang

Cao

et al. 2021

Int J Imaging Syst Tech

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Mass classification of benign and malignant with a new twin support vector machine joint $${l_{2,1}}$$ l 2 , 1 -norm

Cited by 12 publications

References 47 publications

Uncertainty‐guided self‐ensembling model for semi‐supervised segmentation of multiclass retinal fluid in optical coherence tomography images

Uncertainty‐guided self‐ensembling model for semi‐supervised segmentation of multiclass retinal fluid in optical coherence tomography images

Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach

Multiple TBSVM-RFE for the detection of architectural distortion in mammographic images

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