Name
Abstract | ||
|---|---|---|
In this paper we share our solution to the Biomedia ACM MM Grand Challenge 2019, which focuses on the gastrointestinal tract with an aim to detect and classify abnormalities. We firstly identify the challenges in this task, including the scarce and imbalanced data, and the subtle inter-category variances. Based on these analysis, we propose a solution which leverages the 10-fold cross validation approach to alleviate the over-fitting problem, and design a model to adaptively ensemble all sub-models belonging to all component models. Based on extensive offline evaluations, we verify the performance of the proposed technique under various settings. In the competition, we eventually receive the MCC(Matthews correlation coefficient) score of 0.9480.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3343031.3356078 | Proceedings of the 27th ACM International Conference on Multimedia |
Keywords | Field | DocType |
convolutional neural network, deep learning, medical image analysis, the biomedia acm mm grand challenge 2019 | Computer science,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-4503-6889-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhipeng Luo | 1 | 7 | 7.88 |
| Xiao-Wei Wang | 2 | 596 | 59.78 |
| Zhenyu Xu | 3 | 1 | 0.73 |
| Xue Li | 4 | 4 | 1.10 |
| Jiadong Li | 5 | 0 | 1.01 |