A Patch - Based Analysis for Retinal Lesion Segmentation with Deep Neural Networks

2020 ◽  
pp. 677-685
Author(s):  
A Mary Dayana ◽  
W. R. Sam Emmanuel
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Lesion Segmentation ◽  
Retinal Lesion

scholarly journals SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

2021 ◽  
Vol 11 (4) ◽  
pp. 18-21
Author(s):  
Magdalena Michalska
Keyword(s):  
Neural Networks ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Skin Lesions ◽  
Multiclass Classification ◽  
Features Selection ◽  
Lesion Segmentation ◽  
Dermatological Diseases ◽  
Segmentation Process

The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.

Cascaded Deeply Supervised Convolutional Networks for Liver Lesion Segmentation

2021 ◽  
pp. 2152014
Author(s):  
Kaiyi Peng ◽  
Bin Fang ◽  
Mingliang Zhou
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Deep Neural Networks ◽  
Small Volume ◽  
State Of The Art ◽  
Liver Lesion ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Convolutional Networks

Liver lesion segmentation from abdomen computed tomography (CT) with deep neural networks remains challenging due to the small volume and the unclear boundary. To effectively tackle these problems, in this paper, we propose a cascaded deeply supervised convolutional networks (CDS-Net). The cascaded deep supervision (CDS) mechanism uses auxiliary losses to construct a cascaded segmentation method in a single network, focusing the network attention on pixels that are more difficult to classify, so that the network can segment the lesion more effectively. CDS mechanism can be easily integrated into standard CNN models and it helps to increase the model sensitivity and prediction accuracy. Based on CDS mechanism, we propose a cascaded deep supervised ResUNet, which is an end-to-end liver lesion segmentation network. We conduct experiments on LiTS and 3DIRCADb dataset. Our method has achieved competitive results compared with other state-of-the-art ones.

scholarly journals Large deep neural networks for MS lesion segmentation

2017 ◽  
Author(s):  
Juan C. Prieto ◽  
Michele Cavallari ◽  
Miklos Palotai ◽  
Alfredo Morales Pinzon ◽  
Svetlana Egorova ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Lesion Segmentation

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation
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