Previous Article in event
Previous Article in congress
Next Article in event
Next Article in congress
Medical Image Segmentation based on Deep Learning: A Review
Published:
14 July 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress USEDAT.NET: USA-Europe Data Analysis Trends & Complex Networks Mini Congress Series, Coruña, SP-Miami, USA, 2023
Abstract:
This study focuses on utilizing deep learning techniques for segmenting medical images, such as MRI and CT scans. The paper explores the limitations of traditional segmentation methods and highlights the potential of deep learning in overcoming these challenges. It provides an overview of Convolutional Neural Networks (CNNs) and their adaptation for medical image segmentation. Various architectures like U-Net, FCNs, and DeepLab are discussed, along with the importance of data augmentation and handling class imbalance. The paper also covers training processes, post-processing techniques, and evaluation metrics. It concludes by discussing current trends, challenges, and future directions in the field.
Keywords: Medical Images, U-Net, FCNs, DeepLab
Comments on this paper
Shan He
2 January 2024
Dear author(s), Happy New Year 24, Thank you for your contribution to our conference!!!
We have a question for you, you can read and answer bellow.
Question for Authors:
1.How do Convolutional Neural Networks (CNNs), particularly architectures like U-Net, FCNs, and DeepLab, address the limitations of traditional segmentation methods when applied to medical image segmentation, and what specific advantages do these architectures offer in handling complex medical image data?
2.Could you elaborate on the significance of data augmentation and strategies to handle class imbalance in the context of training deep learning models for medical image segmentation, and how do these methods contribute to improving the accuracy and robustness of the segmentation results?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
We have a question for you, you can read and answer bellow.
Question for Authors:
1.How do Convolutional Neural Networks (CNNs), particularly architectures like U-Net, FCNs, and DeepLab, address the limitations of traditional segmentation methods when applied to medical image segmentation, and what specific advantages do these architectures offer in handling complex medical image data?
2.Could you elaborate on the significance of data augmentation and strategies to handle class imbalance in the context of training deep learning models for medical image segmentation, and how do these methods contribute to improving the accuracy and robustness of the segmentation results?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
