Call for Papers

Deep learning for health information mining

Deep learning for leukocyte detection

Deep convolutional network (DNN) learning model for brain tumor detection

Long term short memory (LSTM) model for Collection of crowd sourced medical data for proper scaling in Healthcare

Extreme learning method (ELM) method for medical image analysis Fully convolutional network (FCN) for medical image analysis

Deep learning in Healthcare planning, policy, practice and planning development

Convolutional neural network for image analysis of anatomical structures, functions and lesions

Neural network models for multi-modality fusion for analysis, diagnosis and intervention

Neural networks for medical image reconstruction

Deep Neural networks for molecular, pathologic and cellular image analysis

Neural network models for dynamic, functional and physiologic imaging

Visualization and explainable deep neural network

Deep learning for virtual medical systems

Neural networks for computer-aided detection and diagnosis

Theoretical understanding of deep learning in medical imaging

Transfer learning, disentangling task transfer learning, and multi-task learning

Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)

Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images

Adversarial training on biomedical images and other health data

Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming

Deep Neural networks for medical image retrieval

New model or improved model of convolutional neural network, (e.g., ResNet, DenseNet, Google Inception, etc.)

Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, (etc.)

Optimization by deep neural networks, design of new loss functions (e.g., rank-based loss function)

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