2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDBIO-3.1
Paper Title SU-SAMPLING BASED ACTIVE LEARNING FOR LARGE-SCALE HISTOPATHOLOGY IMAGE
Authors Yiqing Shen, Jing Ke, Shanghai Jiao Tong University, China
SessionBIO-3: Biomedical Signal Processing 3
LocationArea C
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Expensive annotation cost has always been a critical obstacle in deep learning systems, in particular for the applications requiring domain experts' knowledge, such as medical image analysis. Active learning has attracted widespread attention by decreasing the quantity and difficulty in annotation with query strategies. To make a query for labeling, contextual features of candidates' relation are often considered essential. In this paper, we propose an innovative method that incorporating spatial distribution approximation in the uncertainty sampling for whole-slide histopathology image annotation. The active query selection combines the measure of spatial representativeness and histopathological tissue informativeness. With the assumption that the labeling cost of individual instances is non-biased, we use three independent features, namely loss prediction query, Monte Carlo distribution query, and loss estimation enhanced by spatial information for the active learning task. The experiments were validated on large cohorts of The Cancer Genome Atlas (TCGA) and the ''100,000 histological images of human colorectal cancer and healthy tissue'' dataset. Empirically, the proposed method can outperform the other annotation strategies on the histopathology datasets. The annotation cost is reduced by an obvious margin of 40% to retain an accurate classifier. This novel annotation strategy provides the potential to efficiently label and classify histopathology images with a patch-based convolutional neural network.