4 research outputs found
Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism
Segmenting skin lesions from dermoscopic images is essential for diagnosing
skin cancer. But the automatic segmentation of these lesions is complicated due
to the poor contrast between the background and the lesion, image artifacts,
and unclear lesion boundaries. In this work, we present a deep learning model
for the segmentation of skin lesions from dermoscopic images. To deal with the
challenges of skin lesion characteristics, we designed a multi-scale feature
extraction module for extracting the discriminative features. Further in this
work, two attention mechanisms are developed to refine the post-upsampled
features and the features extracted by the encoder. This model is evaluated
using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all
the existing works and the top-ranked models in two competitions
A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images
Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by [Formula: see text] , [Formula: see text] , and classification by [Formula: see text] , [Formula: see text] , respectively than the methods available in the literature