標(biāo)題: Titlebook: Deep Learning and Convolutional Neural Networks for Medical Image Computing; Precision Medicine, Le Lu,Yefeng Zheng,Lin Yang Book 2017 Spr [打印本頁] 作者: minutia 時(shí)間: 2025-3-21 16:51
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing影響因子(影響力)
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing影響因子(影響力)學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing網(wǎng)絡(luò)公開度
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing被引頻次
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing被引頻次學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing年度引用
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing年度引用學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing讀者反饋
書目名稱Deep Learning and Convolutional Neural Networks for Medical Image Computing讀者反饋學(xué)科排名
作者: 包租車船 時(shí)間: 2025-3-21 20:49
Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysisnt role in disease detection and computer-aided decision-making. Machine learning techniques have powered many aspects in medical investigations and clinical practice. Recently, deep learning is emerging a leading machine learning tool in computer vision and begins attracting considerable attentions作者: 小官 時(shí)間: 2025-3-22 04:23 作者: 有抱負(fù)者 時(shí)間: 2025-3-22 05:15 作者: Aggressive 時(shí)間: 2025-3-22 10:19
A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independentocedures. 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作者: Reverie 時(shí)間: 2025-3-22 14:58
Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancerspathological 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作者: Reverie 時(shí)間: 2025-3-22 18:39 作者: 的染料 時(shí)間: 2025-3-23 00:06 作者: 機(jī)警 時(shí)間: 2025-3-23 03:45 作者: Intact 時(shí)間: 2025-3-23 06:09 作者: amygdala 時(shí)間: 2025-3-23 12:44
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作者: 柱廊 時(shí)間: 2025-3-23 17:23 作者: 紳士 時(shí)間: 2025-3-23 18:03
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作者: Peak-Bone-Mass 時(shí)間: 2025-3-24 00:41
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作者: gerrymander 時(shí)間: 2025-3-24 04:02 作者: 厭惡 時(shí)間: 2025-3-24 08:26
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作者: persistence 時(shí)間: 2025-3-24 12:29 作者: 形上升才刺激 時(shí)間: 2025-3-24 17:18
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作者: 多骨 時(shí)間: 2025-3-24 20:22 作者: construct 時(shí)間: 2025-3-24 23:29
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作者: 和諧 時(shí)間: 2025-3-25 04:45
Hans-Peter Hutter,Andreas Ahlenstorfts. The studied models contain five thousand to 160 million parameters and vary in the numbers of layers. Second, we explore the influence of dataset scales and spatial image context configurations on medical image classification performance. Third, when and why transfer learning from the pretrained作者: endure 時(shí)間: 2025-3-25 10:59
Hans-Peter Hutter,Andreas Ahlenstorfsful in various computer vision applications in the last decade. However, this method still takes very long time to detect cells in very small images, e.g., ., albeit it is very effective in the cell detection task. In order to reduce the overall time cost of this method, we combine this method with作者: GULF 時(shí)間: 2025-3-25 12:50
Ayoung Suh,Christy M. K. Cheunglication. Layer-wise fine-tuning may offer a practical way to reach the best performance for the application at hand based on the amount of available data. We conclude that knowledge transfer from natural images is necessary and that the level of tuning should be chosen experimentally.作者: handle 時(shí)間: 2025-3-25 16:11 作者: 報(bào)復(fù) 時(shí)間: 2025-3-25 21:42
Lecture Notes in Computer Scienceand INbreast, where the main conclusion is that both models produce results of similar accuracy, but the CRF model shows faster training and inference. Finally, when compared to the current state of the art in both datasets, the proposed CRF and SSVM models show superior segmentation accuracy.作者: 流浪 時(shí)間: 2025-3-26 02:49
Lecture Notes in Computer Science estimate of the kidney center. Afterwards, we apply MSL to further refine the pose estimate by constraining the position search to a neighborhood around the initial center. The kidney is then segmented using a discriminative active shape model. The proposed method has been trained on 370 CT scans a作者: agenda 時(shí)間: 2025-3-26 07:22
Comparing Android App Permissionsruction and sDAE with both structured labels and discriminative losses to cell detection and segmentation. It is observed that structured learning can effectively handle weak or misleading edges, and discriminative training encourages the model to learn groups of filters that activate simultaneously作者: 典型 時(shí)間: 2025-3-26 11:16 作者: 過濾 時(shí)間: 2025-3-26 15:18 作者: 率直 時(shí)間: 2025-3-26 18:56
Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learnings to obtain a small number of promising candidates, followed by more accurate classification with a deep network. In addition, we propose two approaches, i.e., separable filter decomposition and network sparsification, to speed up the evaluation of a network. To mitigate the over-fitting issue, ther作者: 悲痛 時(shí)間: 2025-3-26 22:30 作者: 古董 時(shí)間: 2025-3-27 04:27 作者: MIRTH 時(shí)間: 2025-3-27 06:39 作者: 緩解 時(shí)間: 2025-3-27 09:26 作者: 鐵砧 時(shí)間: 2025-3-27 15:35
Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and I and appearance of the visual object of interest. We test our methodology on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 left ventricle segmentation challenge dataset and on Japanese Society of Radiological Technology (JSRT) lung segmentation dataset, where our appro作者: overture 時(shí)間: 2025-3-27 19:46
Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammogramsand INbreast, where the main conclusion is that both models produce results of similar accuracy, but the CRF model shows faster training and inference. Finally, when compared to the current state of the art in both datasets, the proposed CRF and SSVM models show superior segmentation accuracy.作者: Solace 時(shí)間: 2025-3-27 22:27 作者: Glucocorticoids 時(shí)間: 2025-3-28 04:28 作者: 引導(dǎo) 時(shí)間: 2025-3-28 07:46 作者: 雪白 時(shí)間: 2025-3-28 13:36 作者: 夾死提手勢(shì) 時(shí)間: 2025-3-28 17:24
2191-6586 ntic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database..978-3-319-82713-1978-3-319-42999-1Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: RAGE 時(shí)間: 2025-3-28 19:10
2191-6586 principles and best practices.Includes supplementary materia.This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on 作者: Longitude 時(shí)間: 2025-3-29 01:55 作者: 特別容易碎 時(shí)間: 2025-3-29 04:10 作者: 密碼 時(shí)間: 2025-3-29 08:19 作者: 細(xì)微的差異 時(shí)間: 2025-3-29 14:18 作者: exclusice 時(shí)間: 2025-3-29 15:38
Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognitionly outperform traditional learning-based approaches while achieving real-time performance. Additionally, we demonstrate that the proposed image classification FCN model can be used in organ localisation task as well without requiring additional training data.作者: Femish 時(shí)間: 2025-3-29 22:15 作者: 不整齊 時(shí)間: 2025-3-30 02:11 作者: chondromalacia 時(shí)間: 2025-3-30 07:32 作者: Nefarious 時(shí)間: 2025-3-30 09:15 作者: CAB 時(shí)間: 2025-3-30 16:01
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.作者: OTHER 時(shí)間: 2025-3-30 16:59
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.作者: RENIN 時(shí)間: 2025-3-30 23:00
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作者: 擦試不掉 時(shí)間: 2025-3-31 03:06 作者: 羅盤 時(shí)間: 2025-3-31 05:29 作者: Obedient 時(shí)間: 2025-3-31 11:35
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 作者: macular-edema 時(shí)間: 2025-3-31 15:58
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作者: RAFF 時(shí)間: 2025-3-31 17:42
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作者: Acumen 時(shí)間: 2025-3-31 23:31
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
