MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network
Authors
Keywords
Multiple max-pooling integration module, Cross multiscale deconvolution network, Deep learning, End-to-end
Journal
KNOWLEDGE-BASED SYSTEMS
Volume 231, Issue -, Pages 107456
Publisher
Elsevier BV
Online
2021-09-01
DOI
10.1016/j.knosys.2021.107456
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Medical image segmentation based on active fusion-transduction of multi-stream features
- (2021) Yucheng Shu et al. KNOWLEDGE-BASED SYSTEMS
- DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds
- (2021) Jeonghoon Kwak et al. Remote Sensing
- PyDiNet: Pyramid Dilated Network for medical image segmentation
- (2021) Mourad Gridach NEURAL NETWORKS
- Feature-transfer network and local background suppression for microaneurysm detection
- (2020) Xinpeng Zhang et al. MACHINE VISION AND APPLICATIONS
- CNN-Based Multilayer Spatial–Spectral Feature Fusion and Sample Augmentation With Local and Nonlocal Constraints for Hyperspectral Image Classification
- (2019) Jie Feng et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- DRINet for Medical Image Segmentation
- (2018) Liang Chen et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
- (2018) Ozan Oktay et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation
- (2018) Tsung-Chen Chiang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Multiple Resolution Residually Connected Feature Streams For Automatic Lung Tumor Segmentation From CT Images
- (2018) Jue Jiang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning
- (2018) IEEE TRANSACTIONS ON MEDICAL IMAGING
- Online Tracker Optimization for Multi-Pedestrian Tracking Using a Moving Vehicle Camera
- (2018) Sang Jun Kim et al. IEEE Access
- Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches
- (2017) Jan L. Bruse et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration
- (2017) Marie Bieth et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images
- (2017) Ozan Oktay et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
- (2016) Sergio Pereira et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
- (2016) Hoo-Chang Shin et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Recommender system application developments: A survey
- (2015) Jie Lu et al. DECISION SUPPORT SYSTEMS
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
- (2015) Bjoern H. Menze et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Transfer learning using computational intelligence: A survey
- (2015) Jie Lu et al. KNOWLEDGE-BASED SYSTEMS
- Dental R-Ray Image Segmentation Using Texture Recognition
- (2014) Pedro Henrique Marques Lira et al. IEEE Latin America Transactions
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started