scholarly journals A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

2021 ◽  
Vol 8 ◽  
Author(s):  
Hongliang He ◽  
Chi Zhang ◽  
Jie Chen ◽  
Ruizhe Geng ◽  
Luyang Chen ◽  
...  
Keyword(s):  
Image Analysis ◽  
State Of The Art ◽  
The State ◽  
Dense Network ◽  
Crucial Step ◽  
Full Resolution ◽  
Proposed Model ◽  
Computer Aided ◽  
Histopathological Images ◽  
Nuclear Segmentation

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.

The State of the Art in Biological Image Analysis

Cell Biology ◽  
2006 ◽  
pp. 201-206
Author(s):  
F FEDERICI ◽  
S SCAGLIONE ◽  
A DIASPRO
Keyword(s):  
Image Analysis ◽  
State Of The Art ◽  
The State ◽  
Biological Image

Content Integration in Games-Based Learning Systems

2009 ◽  
pp. 73-83
Author(s):  
Marco A. Gómez-Martín ◽  
Pedro P. Gómez-Martín ◽  
Pedro A. González-Calero
Keyword(s):  
State Of The Art ◽  
The State ◽  
Point Of View ◽  
Learning Systems ◽  
Authoring Tools ◽  
Computer Aided Instruction ◽  
Content Creation ◽  
Domain Experts ◽  
Instructional Content ◽  
Computer Aided

A key challenge to move forward the state of the art in games-based learning systems is to facilitate instructional content creation by the domain experts. Several decades of research on computer aided instruction have demonstrated that the expert has to be deeply involved in the content creation process, and that is why so much effort has been devoted to building authoring tools of all kinds. However, using videogame technology to support computer aided instruction poses some new challenges on expertfriendly authoring tools, related to technical and cost issues. In this chapter the authors present the state of the art in content creation for games-based learning systems, identifying the main challenges to make this technology cost-effective from the content creation point of view.

scholarly journals Appearance and Motion Enhancement for Video-Based Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 11394-11401
Author(s):  
Shuzhao Li ◽  
Huimin Yu ◽  
Haoji Hu
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Complex Model ◽  
The State ◽  
Final Model ◽  
Backbone Network ◽  
Proposed Model ◽  
Art Performance ◽  
Attribute Recognition

In this paper, we propose an Appearance and Motion Enhancement Model (AMEM) for video-based person re-identification to enrich the two kinds of information contained in the backbone network in a more interpretable way. Concretely, human attribute recognition under the supervision of pseudo labels is exploited in an Appearance Enhancement Module (AEM) to help enrich the appearance and semantic information. A Motion Enhancement Module (MEM) is designed to capture the identity-discriminative walking patterns through predicting future frames. Despite a complex model with several auxiliary modules during training, only the backbone model plus two small branches are kept for similarity evaluation which constitute a simple but effective final model. Extensive experiments conducted on three popular video-based person ReID benchmarks demonstrate the effectiveness of our proposed model and the state-of-the-art performance compared with existing methods.

scholarly journals A modified Bayesian Convolutional Neural Network for Breast Histopathology Image Classification and Uncertainty Quantification

2020 ◽  
Author(s):  
Pushkar Khairnar ◽  
Ponkrshnan Thiagarajan ◽  
Susanta Ghosh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Uncertainty Quantification ◽  
State Of The Art ◽  
False Negative ◽  
Activation Function ◽  
Ensemble Averaging ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Histopathological Images

Convolutional neural network (CNN) based classification models have been successfully used on histopathological images for the detection of diseases. Despite its success, CNN may yield erroneous or overfitted results when the data is not sufficiently large or is biased. To overcome these limitations of CNN and to provide uncertainty quantification Bayesian CNN is recently proposed. However, we show that Bayesian-CNN still suffers from inaccuracies, especially in negative predictions. In the present work, we extend the Bayesian-CNN to improve accuracy and the rate of convergence. The proposed model is called modified Bayesian-CNN. The novelty of the proposed model lies in an adaptive activation function that contains a learnable parameter for each of the neurons. This adaptive activation function dynamically changes the loss function thereby providing faster convergence and better accuracy. The uncertainties associated with the predictions are obtained since the model learns a probability distribution on the network parameters. It reduces overfitting through an ensemble averaging over networks, which in turn improves accuracy on the unknown data. The proposed model demonstrates significant improvement by nearly eliminating overfitting and remarkably reducing (about 38%) the number of false-negative predictions. We found that the proposed model predicts higher uncertainty for images having features of both the classes. The uncertainty in the predictions of individual images can be used to decide when further human-expert intervention is needed. These findings have the potential to advance the state-of-the-art machine learning-based automatic classification for histopathological images.

scholarly journals Depthwise Convolution Is All You Need for Learning Multiple Visual Domains

2019 ◽  
Vol 33 ◽  
pp. 8368-8375 ◽  
Author(s):  
Yunhui Guo ◽  
Yandong Li ◽  
Liqiang Wang ◽  
Tajana Rosing
Keyword(s):  
State Of The Art ◽  
The State ◽  
Spatial Correlations ◽  
Single Model ◽  
Domain Specific ◽  
Gating Mechanism ◽  
Proposed Model ◽  
Domain Learning ◽  
Domain Models ◽  
Universal Structure

There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.

Tutorial: Deep Learning Advancing the State-of-the-Art in Medical Image Analysis

2017 ◽  
pp. 6-7 ◽  
Author(s):  
Vincent Christlein ◽  
Florin C. Ghesu ◽  
Tobias Würfl ◽  
Andreas Maier ◽  
Fabian Isensee ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
The State
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