Image Complexity Guided Network Compression for Biomedical Image Segmentation

Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed architecture typically involves a series of time-consuming training/validation experiments to determine a good compromise between network size and performance accuracy. To address this, we propose an image complexity-guided network compression technique for biomedical image segmentation. Given any resource constraints, our framework utilizes data complexity and network architecture to quickly estimate a compressed model which does not require network training. Specifically, we map the dataset complexity to the target network accuracy degradation caused by compression. Such mapping enables us to predict the final accuracy for different network sizes, based on the computed dataset complexity. Thus, one may choose a solution that meets both the network size and segmentation accuracy requirements. Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network. We conduct experiments using 5 datasets, employing 3 commonly-used CNN architectures for biomedical image segmentation as representative networks. Our proposed framework is shown to be effective for generating compressed segmentation networks, retaining up to ≈95% of the full-sized network segmentation accuracy, and at the same time, utilizing ≈32x fewer network trainable weights (average reduction) of the full-sized networks.

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CC-NET: Image Complexity Guided Network Compression for Biomedical Image Segmentation

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) ◽

10.1109/isbi.2019.8759448 ◽

2019 ◽

Cited By ~ 3

Author(s):

Suraj Mishra ◽

Peixian Liang ◽

Adam Czajka ◽

Danny Z. Chen ◽

X. Sharon Hu

Keyword(s):

Image Segmentation ◽

Image Complexity ◽

Biomedical Image ◽

Network Compression

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Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽

10.3390/a14050144 ◽

2021 ◽

Vol 14 (5) ◽

pp. 144

Author(s):

Yuexing Han ◽

Xiaolong Li ◽

Bing Wang ◽

Lu Wang

Keyword(s):

Image Segmentation ◽

Spatial Information ◽

Medical Images ◽

Tumor Segmentation ◽

Convolutional Networks ◽

Learning Framework ◽

Fully Convolutional Networks ◽

Segmentation Accuracy ◽

Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

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A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification

Applied Sciences ◽

10.3390/app11114880 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4880

Author(s):

Abigail Copiaco ◽

Christian Ritz ◽

Nidhal Abdulaziz ◽

Stefano Fasciani

Keyword(s):

Network Architecture ◽

Single Channel ◽

Classification Performance ◽

Network Size ◽

Directed Acyclic Graphs ◽

Spectral Features ◽

Audio Classification ◽

Resource Requirements ◽

Efficient Alternative ◽

Computational Resources

Recent methodologies for audio classification frequently involve cepstral and spectral features, applied to single channel recordings of acoustic scenes and events. Further, the concept of transfer learning has been widely used over the years, and has proven to provide an efficient alternative to training neural networks from scratch. The lower time and resource requirements when using pre-trained models allows for more versatility in developing system classification approaches. However, information on classification performance when using different features for multi-channel recordings is often limited. Furthermore, pre-trained networks are initially trained on bigger databases and are often unnecessarily large. This poses a challenge when developing systems for devices with limited computational resources, such as mobile or embedded devices. This paper presents a detailed study of the most apparent and widely-used cepstral and spectral features for multi-channel audio applications. Accordingly, we propose the use of spectro-temporal features. Additionally, the paper details the development of a compact version of the AlexNet model for computationally-limited platforms through studies of performances against various architectural and parameter modifications of the original network. The aim is to minimize the network size while maintaining the series network architecture and preserving the classification accuracy. Considering that other state-of-the-art compact networks present complex directed acyclic graphs, a series architecture proposes an advantage in customizability. Experimentation was carried out through Matlab, using a database that we have generated for this task, which composes of four-channel synthetic recordings of both sound events and scenes. The top performing methodology resulted in a weighted F1-score of 87.92% for scalogram features classified via the modified AlexNet-33 network, which has a size of 14.33 MB. The AlexNet network returned 86.24% at a size of 222.71 MB.

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A Densely Connected Network Based on U-Net for Medical Image Segmentation

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3446618 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-14

Author(s):

Zhenzhen Yang ◽

Pengfei Xu ◽

Yongpeng Yang ◽

Bing-Kun Bao

Keyword(s):

Feature Extraction ◽

Image Segmentation ◽

Loss Function ◽

Network Architecture ◽

Medical Image ◽

Medical Image Segmentation ◽

Cross Entropy ◽

Loss Functions ◽

Feature Maps ◽

Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

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SEAUNet:Domain Adaptation for Biomedical Image Segmentation

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm49941.2020.9313161 ◽

2020 ◽

Author(s):

Hang Chen ◽

Leiting Chen ◽

Yuchu Chen ◽

Minghao Fan ◽

Chuan Zhou

Keyword(s):

Image Segmentation ◽

Biomedical Image

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Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm49941.2020.9313254 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiawei Zhang ◽

Yanchun Zhang ◽

Shanfeng Zhu ◽

Xiaowei Xu

Keyword(s):

Image Segmentation ◽

Biomedical Image ◽

Multi Scale

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PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

BMC Bioinformatics ◽

10.1186/s12859-020-03943-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Changyong Li ◽

Yongxian Fan ◽

Xiaodong Cai

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

State Of The Art ◽

Experimental Results ◽

Actual Situation ◽

Controlled Experiments ◽

Biomedical Image ◽

Segmentation Methods ◽

Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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