scholarly journals Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning

2021 ◽  
pp. 34-43
Author(s):  
Sora Iwamoto ◽  
Bisser Raytchev ◽  
Toru Tamaki ◽  
Kazufumi Kaneda
Keyword(s):  
Medical Images ◽  
Uncertainty Modeling ◽  
Semantic Segmentation ◽  
Deep Networks

scholarly journals Scene-Aware Deep Networks for Semantic Segmentation of Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69184-69193 ◽  
Author(s):  
Zhike Yi ◽  
Tao Chang ◽  
Shuai Li ◽  
Ruijun Liu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Deep Networks ◽  
Segmentation Of Images

scholarly journals A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

2019 ◽  
Vol 11 (6) ◽  
pp. 684 ◽  
Author(s):  
Maria Papadomanolaki ◽  
Maria Vakalopoulou ◽  
Konstantinos Karantzalos
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Novel Object ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Object Based ◽  
Deep Networks ◽  
Very High

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images

2020 ◽  
Vol 24 (11) ◽  
pp. 3215-3225 ◽  
Author(s):  
Liangliang Liu ◽  
Fang-Xiang Wu ◽  
Yu-Ping Wang ◽  
Jianxin Wang
Keyword(s):  
Receptive Field ◽  
Medical Images ◽  
Semantic Segmentation

scholarly journals Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qiang Lin ◽  
Tongtong Li ◽  
Chuangui Cao ◽  
Yongchun Cao ◽  
Zhengxing Man ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Original Data ◽  
Spect Imaging ◽  
Fine Tuning ◽  
Geometric Transformation ◽  
Nuclear Medicine Imaging ◽  
Automated Diagnosis ◽  
Deep Networks

AbstractSPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the convolutional neural networks, has been widely applied to the classification of medical images. In order to reliably classify SPECT bone images for the automated diagnosis of metastasis on which the SPECT imaging solely focuses, in this paper, we present several deep classifiers based on the deep networks. Specifically, original SPECT images are cropped to extract the thoracic region, followed by a geometric transformation that contributes to augment the original data. We then construct deep classifiers based on the widely used deep networks including VGG, ResNet and DenseNet by fine-tuning their parameters and structures or self-defining new network structures. Experiments on a set of real-world SPECT bone images show that the proposed classifiers perform well in identifying bone metastasis with SPECT imaging. It achieves 0.9807, 0.9900, 0.9830, 0.9890, 0.9802 and 0.9933 for accuracy, precision, recall, specificity, F-1 score and AUC, respectively, on the test samples from the augmented dataset without normalization.

scholarly journals Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

2020 ◽  
Author(s):  
Samik Banerjee ◽  
Lucas Magee ◽  
Dingkang Wang ◽  
Xu Li ◽  
Bingxing Huo ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Large Scale ◽  
Image Data ◽  
Semantic Segmentation ◽  
Scientific Data ◽  
Error Rates ◽  
Topological Data Analysis ◽  
Hybrid Architecture ◽  
Deep Networks

Understanding of neuronal circuitry at cellular resolution within the brain has relied on tract tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

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