ResUNet++: An Advanced Architecture for Medical Image Segmentation

Jha, Debesh; Smedsrud, Pia H.; Riegler, Michael; Johansen, Dag; Lange, Thomas de; Halvorsen, Pål; Johansen, Håvard D.

doi:10.48550/arxiv.1911.07067

Cited by 5 publications

(10 citation statements)

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“…ResUnet++ [26] (2019) 0.810 0.879 0.904 0.883 0.882 0.891 CE-Net [27] (2019) 0.821 0.884 0.891 0.887 0.901 0.892 CG-Net [28] (2020) 0.859 0.921 0.917 0.909 0.904 0.914…”

Section: Resultsmentioning

confidence: 99%

The application research of deep neural networks in colonic polyp segmentation

Wang

Yang

et al. 2022

Preprint

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Background: In the study of medical images segmentation, U-Net is a good choice with better performance. But it will cause some problems such as gradient problems or information loss. Methods: In this paper, we introduce a new model based on such an encoder-decoder structure. We mixed a full connection and encoder output in per layer and we add an axial attention to solve gradient problem and keep the connection with pixel from long distance. After pooling, we add a module which has three atrous Convolution processes with different expansion rate and a non-local Self attention to increase the feeling field and fix the problem of losing spatial information. In decoder part, we put a dual channel gate to point out 'what' and 'where' feature we need by the union of Channel Attention and Spatial Attention. Results: We use 4 public dataSets are CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB and CVC-T. We compared more than 10 groups other different models with these 4 group dataSets and marked some evaluating indicators such as mIou, mDice and so on. We also did some ablation experiments to verify that our model structure is reasonable and effective. Conclusions: The comparative training of 12 models in 4 different datasets verifies the feasibility and the good effectiveness of the proposed ideas. We also did some ablation experiments to verify that our model structure is reasonable and effective. The basic structure of the encoder decoder in semantic segmentation has been greatly improved.

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“…ResUnet++ [26] (2019) 0.810 0.879 0.904 0.883 0.882 0.891 CE-Net [27] (2019) 0.821 0.884 0.891 0.887 0.901 0.892 CG-Net [28] (2020) 0.859 0.921 0.917 0.909 0.904 0.914…”

Section: Resultsmentioning

confidence: 99%

The application research of deep neural networks in colonic polyp segmentation

Wang

Yang

et al. 2022

Preprint

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“…For the evaluation, the detailed models in image segmentation and image classification were selected based on the performance and the number of citations [ 17 , 18 , 19 ]. The applied datasets were based on the most usable datasets among the open datasets [ 32 , 33 , 34 ].…”

Section: Breast Cancer Diagnosis Process and Related Image Recognitio...mentioning

confidence: 99%

“…U-Net [ 17 ], ResU-Net++ [ 18 ], and DeepLabV3 [ 19 ], which are the most widely known image segmentation networks for X-ray image and ultrasound image diagnosis, are used to verify and optimize performance.…”

Section: Breast Cancer Diagnosis Process and Related Image Recognitio...mentioning

confidence: 99%

“…For this goal, the image recognition technologies for optimizing the diagnosis process using each medical image data are investigated in terms of various image classification techniques (VGGNet19 [ 1 ], ResNet50 [ 2 ], DenseNet121 [ 3 ], and EfficientNet v2 [ 4 ]), image segmentation techniques (U-Net [ 17 ], ResU-Net++ [ 18 ], and DeepLabV3 [ 19 ]), and related loss functions (Binary Cross Entropy, Dice Loss, Tversky Loss), and data augmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, mammography (examination of human breast using low energy X-ray) images, ultrasound images, and histopathology (microscopic examination of tissue) images are used to improve the accuracy of the process of diagnosing breast cancer classification through image classification technique and of inferring their affected location through image segmentation technique. Then, the image recognition application strategies for optimizing the diagnosis process using each medical image data are investigated in terms of various image classification techniques (VGGNet19 [ 1 ], ResNet50 [ 2 ], DenseNet121 [ 3 ], and EfficientNet v2 [ 4 ]), image segmentation techniques (U-Net [ 17 ], ResU-Net++ [ 18 ], and DeepLabV3 [ 19 ]), and related loss functions (Binary Cross Entropy, Dice Loss, Tversky Loss), and data augmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

Kwak¹,

Choi²,

Lee³

2023

Sensors

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.

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