-
Automatic segmentation of skin lesions using deep learning
This paper summarizes the method used in our submission to Task 1 of the...
read it
-
Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations
Lesion segmentation from the surrounding skin is the first task for deve...
read it
-
Global and Local Information Based Deep Network for Skin Lesion Segmentation
With a large influx of dermoscopy images and a growing shortage of derma...
read it
-
Lesion segmentation using U-Net network
This paper explains the method used in the segmentation challenge (Task ...
read it
-
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Argumentation
Segmentation of skin lesions is considered as an important step in compu...
read it
-
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation
Segmentation of skin lesions is considered as an important step in compu...
read it
-
A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net
White matter hyperintensity (WMH) is commonly found in elder individuals...
read it
The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation
Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for developing automated skin lesion segmentation. This paper investigates various pre- and post-processing techniques for improving the performance of U-Nets as measured by the Jaccard Index. The dataset provided as part of the "2017 ISBI Challenges on Skin Lesion Analysis Towards Melanoma Detection" was used for this evaluation and the performance of the finalist competitors was the standard for comparison. The pre-processing techniques employed in the proposed system included contrast enhancement, artifact removal, and vignette correction. More advanced image transformations, such as local binary patterns and wavelet decomposition, were also employed to augment the raw grayscale images used as network input features. While the performance of the proposed system fell short of the winners of the challenge, it was determined that using wavelet decomposition as an early transformation step improved the overall performance of the system over pre- and post-processing steps alone.
READ FULL TEXT
Comments
There are no comments yet.