Machine Learning in Medical Imaging
Springer International Publishing (Verlag)
978-3-030-32691-3 (ISBN)
The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
rain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization.- Spatial Regularized Classification Network for Spinal Dislocation Diagnosis.- Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.- Advancing Pancreas Segmentation in Multi-protocol MRI Volumes using Hausdorff-Sine Loss Function.- WSI-Net: Branch-based and Hierarchy-aware Network for Segmentation and Classification of Breast Histopathological Whole-slide Images.- Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information.- MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network.- DCCL: A Benchmark for Cervical Cytology Analysis.- Smartphone-Supported Malaria Diagnosis Based on Deep Learning.- Children's Neuroblastoma Segmentation using Morphological Features.- GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images.- Deep Active Lesion Segmentation.- Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning.- A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification.- End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation.- Privacy-preserving Federated Brain Tumour Segmentation.- Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images.- Semi-Supervised Multi-Task Learning With Chest X-Ray Images.- Novel Bi-directional Images Synthesis based on WGAN-GP with GMM-based Noise Generation.- Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett's Esophagus.- Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images.- Boundary Aware Networks for Medical Image Segmentation.- Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks.- Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration.- Joint Shape Representation and Classification for Detecting PDAC.- FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans.- Weakly supervised segmentation by a deep geodesic prior.- Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks.- Correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps.- Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI.- Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation.- Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-Unet.- Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images.- Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation.- Detecting abnormalities in resting-state dynamics: An unsupervised learning approach.- Distanced LSTM: Time-Distanced Gates in Long Short-Term MemoryModels for Lung Cancer Detection.- Dense-residual Attention Network for Skin Lesion Segmentation.- Confounder-Aware Visualization of ConvNets.- Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks.- Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection.- Unsupervised Lesion Detection with Locally Gaussian Approximation.- A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection.- BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks.- Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI.- Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI.- A Maximum Entropy Deep Reinforcement Learning Neural Tracker.- Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation.- Select, Attend, and Transfer: Light, Learnable Skip Connections.- Learning-based Bone Quality Classification Method for Spinal Met
| Erscheinungsdatum | 11.10.2019 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XVIII, 695 p. 310 illus., 245 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1080 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Applications • Artificial Intelligence • automatic segmentations • Computer Science • conference proceedings • CT image • Image Analysis • image reconstruction • Image Registration • Image Segmentation • Informatics • Learning Algorithms • Medical Images • Neural networks • Recurrent Neural Networks • Research • segmentation methods • Support Vector Machines (SVM) |
| ISBN-10 | 3-030-32691-8 / 3030326918 |
| ISBN-13 | 978-3-030-32691-3 / 9783030326913 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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