Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

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A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015909 ◽

2019 ◽

Vol 33 ◽

pp. 5909-5916 ◽

Cited By ~ 6

Author(s):

Hao Zheng ◽

Yizhe Zhang ◽

Lin Yang ◽

Peixian Liang ◽

Zhuo Zhao ◽

...

Keyword(s):

Image Segmentation ◽

Ensemble Learning ◽

State Of The Art ◽

3D Models ◽

Superior Performance ◽

3D Image ◽

Convolutional Network ◽

Biomedical Image ◽

Learning Framework ◽

2D And 3D

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the baselearners as multiple versions of “ground truths”. Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semisupervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.

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DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2021.3052320 ◽

2021 ◽

pp. 1-1

Author(s):

Ziyuan Zhao ◽

Zeng Zeng ◽

Kaixin Xu ◽

Cen Chen ◽

Cuntai Guan

Keyword(s):

Image Segmentation ◽

Active Learning ◽

Biomedical Image

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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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Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc45102.2020.9294260 ◽

2020 ◽

Author(s):

Weitao Chen ◽

Rick Salay ◽

Sean Sedwards ◽

Vahdat Abdelzad ◽

Krzysztof Czarnecki

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Active Learning ◽

Convolutional Neural Networks

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Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽

10.3390/chemosensors9040078 ◽

2021 ◽

Vol 9 (4) ◽

pp. 78

Author(s):

Jianhua Cao ◽

Tao Liu ◽

Jianjun Chen ◽

Tao Yang ◽

Xiuxiu Zhu ◽

...

Keyword(s):

Active Learning ◽

Gas Sensor ◽

Electronic Nose ◽

Effective Means ◽

Learning Paradigm ◽

Class Label ◽

Learning Framework ◽

Sensor Drift ◽

Drift Compensation ◽

Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

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