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

amygdala

On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imagingodalities, and studied the necessity of fine-tuned CNNs under varying amounts of training data. Second, . In response, we proposed a layer-wise fine-tuning scheme to examine how the extent or depth of fine-tuning contributes to the success of knowledge transfer. Our experiments consistently showed t

柱廊

紳士

Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammogramsgnal-to-noise ratio of their appearance. We address this problem with structured output prediction models that use potential functions based on deep convolution neural network (CNN) and deep belief network (DBN). The two types of structured output prediction models that we study in this work are the

Peak-Bone-Mass

Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image C disease diagnosis and quantification. However, automatic pathological kidney segmentation is still a challenging task due to large variations in contrast phase, scanning range, pathology, and position in the abdomen, etc. Methods based on global image context (e.g., atlas- or regression-based appro

gerrymander

厭惡

Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labelingdetection of pathologies, surgical assistance as well as computer-aided diagnosis (CAD). In general, the large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges of organ segme

persistence

形上升才刺激

Yuan Feng,Yadie Rao,RongRong Fubility scores for lesions (or pathology). We found that this second stage is a highly selective classifier that is able to reject difficult false positives while retaining good sensitivity rates. The method was evaluated on three data sets (sclerotic metastases, lymph nodes, colonic polyps) with var

多骨

construct

Andrea Valente,Emanuela Marchettiegies. In this chapter, we present deep learning based approaches for two challenged tasks in histological image analysis: (1) Automated nuclear atypia scoring (NAS) on breast histopathology. We present a Multi-Resolution Convolutional Network (MR-CN) with Plurality Voting (MR-CN-PV) model for autom