Titlebook: Deep Learning and Convolutional Neural Networks for Medical Image Computing; Precision Medicine, Le Lu,Yefeng Zheng,Lin Yang Book 2017 Spr

Nefarious

CAB

A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independentrmalized as a maximum-weight independent set problem, which is designed to find the heaviest subset of mutually nonadjacent nodes in a graph. Experiments show that the proposed general cell detection algorithm provides detection results that are dramatically better than any individual cell detection algorithm.

OTHER

Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, UD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose three algorithms based on deep convolutional neural networks (CNNs). The differences between the two main publicly available datasets are discussed as well.

RENIN

Zackarias Alenljung,Jessica Lindblomatasets are leading to dramatic advances in automated understanding of medical images. From this perspective, I give a personal view of how computer-aided diagnosis of medical images has evolved and how the latest advances are leading to dramatic improvements today. I discuss the impact of deep lear

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羅盤

Obedient

Satu Jumisko-Pyykk?,Gail Kenningt. However, most of the published work has been confined to solving 2D problems, with a few limited exceptions that treated the 3D space as a composition of 2D orthogonal planes. The challenge of 3D deep learning is due to a much larger input vector, compared to 2D, which dramatically increases the

macular-edema

Yuan Feng,Yadie Rao,RongRong Fuocedures. In this chapter, we propose a novel algorithm for general cell detection problem: First, a set of cell detection candidates is generated using different algorithms with varying parameters. Second, each candidate is assigned a score by a trained deep convolutional neural network (DCNN). Fin

RAFF

Andrea Valente,Emanuela Marchettipathological tissue specimens. For a number of cancers, the clinical cancer grading system is highly correlated with the pathomic features of histologic primitives that appreciated from histopathological images. However, automated detection and segmentation of histologic primitives is pretty challen

Acumen

Lecture Notes in Computer Sciencelutions rely on manually provided regions of interest, limiting their clinical usefulness. We focus on two challenges currently existing in two publicly available datasets. First of all, missed labeling of regions of interest is a common issue in existing medical image datasets due to the labor-inte