Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures
Authors
Keywords
-
Journal
Diagnostics
Volume 13, Issue 3, Pages 546
Publisher
MDPI AG
Online
2023-02-03
DOI
10.3390/diagnostics13030546
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Cancer statistics, 2022
- (2022) Rebecca L. Siegel et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method
- (2022) Akitoshi Shimazaki et al. Scientific Reports
- Teacher-student approach for lung tumor segmentation from mixed-supervised datasets
- (2022) Vemund Fredriksen et al. PLoS One
- SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans
- (2022) Mehmet Akif Cifci Applied Bionics and Biomechanics
- The Medical Segmentation Decathlon
- (2022) Michela Antonelli et al. Nature Communications
- Deep Learning Techniques to Diagnose Lung Cancer
- (2022) Lulu Wang Cancers
- Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2021) Hyuna Sung et al. CA-A CANCER JOURNAL FOR CLINICIANS
- State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
- (2021) Saqib Ali et al. Cancers
- Lung Cancer 2020
- (2020) Brett C. Bade et al. CLINICS IN CHEST MEDICINE
- Improving breast mass classification by shared data with domain transformation using a generative adversarial network
- (2020) Chisako Muramatsu et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification
- (2020) K. Vijila Rani et al. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
- Radiomics and deep learning in lung cancer
- (2020) Michele Avanzo et al. STRAHLENTHERAPIE UND ONKOLOGIE
- Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest
- (2020) May Phu Paing et al. Applied Sciences-Basel
- Automated lung cancer diagnosis using three-dimensional convolutional neural networks
- (2020) Gustavo Perez et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Evaluation of deep learning‐based auto‐segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients
- (2020) Zhi Wang et al. Journal of Applied Clinical Medical Physics
- Automated 3-D Lung Tumor Detection and Classification by an Active Contour Model and CNN Classifier
- (2019) Gopi Kasinathan et al. EXPERT SYSTEMS WITH APPLICATIONS
- Surgical stress and cancer progression: the twisted tango
- (2019) Zhiwei Chen et al. Molecular Cancer
- Chemotherapy remains a cornerstone in the treatment of nonsmall cell lung cancer
- (2019) Robert Pirker CURRENT OPINION IN ONCOLOGY
- Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation
- (2018) Philipp Tschandl et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Lung nodule detection and classification based on geometric fit in parametric form and deep learning
- (2018) Syed Muhammad Naqi et al. NEURAL COMPUTING & APPLICATIONS
- Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT
- (2018) Yutong Xie et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Trends and advances in tumor immunology and lung cancer immunotherapy
- (2016) Mohanad Aldarouish et al. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
- A critical review of recent developments in radiotherapy for non-small cell lung cancer
- (2016) Sarah Baker et al. Radiation Oncology
- Important prognostic factors for the long-term survival of lung cancer subjects in Taiwan
- (2008) Tai-An Chiang et al. BMC CANCER
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now