Deep Learning for Medical Image Analysis (2017-08-17T00:00:00.000000Z)

TL;DR

Different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation for analyzing medical images using deep learning algorithm are explored.

Abstract

This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm. In this report, as an example, we explore different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation. This report prepared for the doctoral consortium in the AIME-2017 conference.

Authors

Mina Rezaei

1 papers

Haojin Yang

1 papers

C. Meinel

1 papers

Deep Learning for Medical Image Analysis

TL;DR

Abstract

Authors

References13 items

Deep Learning for Medical Image Analysis

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Instance-Aware Semantic Segmentation via Multi-task Network Cascades

Deep Residual Learning for Image Recognition

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

You Only Look Once: Unified, Real-Time Object Detection

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Fast R-CNN

Fully convolutional networks for semantic segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

ImageNet classification with deep convolutional neural networks

Deep Learning

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