Deep Learning for Medical Image Analysis
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Summary
Different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation for analyzing medical images using deep learning algorithm are explored.
- Type
- preprint
- Published
- 2017-08-17
- Cited by
- 2,709
- References
- 13
- Access
- Open access
- OpenAlex
- https://openalex.org/W4306321919
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7961631
Keywords
Deep learning, Artificial intelligence, Segmentation, Computer science, Abnormality
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- You Only Look Once: Unified, Real-Time Object Detection
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Fully convolutional networks for semantic segmentation
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition
- Instance-Aware Semantic Segmentation via Multi-task Network Cascades
- Deep Learning for Medical Image Analysis
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- Deep Learning
- Fast R-CNN
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- Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
- MagNet: A Two-Pronged Defense against Adversarial Examples
- DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
- Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
- Machine Learning Methods for Histopathological Image Analysis
- A Deep Learning Approach to Estimate Chemically-Treated Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images
- Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach
- Large‐scale retrieval for medical image analytics: A comprehensive review
- Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review
- A Survey on Computer Vision for Assistive Medical Diagnosis From Faces
- Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches
- Learning to diagnose from scratch by exploiting dependencies among labels
- Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI
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