A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms-Reference-Cited by-同舟云学术

A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

Published:2023 Issue: Volume:11 Page:6078-6091
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hernandez Lopez Juanita¹^ORCID,Azuela Juan Humberto Sossa¹^ORCID,Varela Alberto Salvador Nunez²,Gamez Cesar Augusto Ramirez²,Gonzalez Virginia Canseco³,Perez Francisco Eduardo Martinez²,Munoz Sandra Edith Nava²^ORCID,Gonzalez Hector Gerardo Perez²^ORCID,Varela Jose Ignacio Nunez²

Affiliation:

1. Centro de Investigación en Computación del Instituto Politécnico Nacional, Mexico City, Mexico

2. Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, San Luis Potosi, Mexico

3. Hospital Central Dr. Ignacio Morones Prieto, San Luis Potosi, Mexico

Funder

Consejo Nacional de Ciencia y Tecnolog?a

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10016691.pdf?arnumber=10016691

Reference42 articles.

1. Review the state-of-the-art technologies of semantic segmentation based on deep learning

2. Deep learning based pectoral muscle segmentation on Mammographic Image Analysis Society (MIAS) mammograms

3. Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+

4. PeMNet for Pectoral Muscle Segmentation

5. Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network