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Classification of blood cancer images using a convolutional neural networks ensemble
Author(s): Kaiqiang Ma; Lingling Sun; Yaqi Wang; Junchao Wang
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Paper Abstract

This paper presents the method of our submission to the ISBI 2019 Challenge for the task of classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. We aimed to combine convolutional neural networks with several state-of-the-art techniques. Specifically, we fine-tuned pretrained deep learning networks including ResNet and DenseNet for this task. Overfitting is one of the major problems for this challenge. We solve overfitting by using the gradient norm clipping and the cosine annealing learning rate schedule with restarts, which have a significant impact on the performance of our deep neural network. More importantly, adaptive pooling layer is used in our models. With this modification, models are able to adapt to images of any size. An ensemble of deep models achieved a 0.8570 weighted-f1 score on the preliminary test set reported by the test server.

Paper Details

Date Published: 14 August 2019
PDF: 6 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117903 (14 August 2019); doi: 10.1117/12.2539605
Show Author Affiliations
Kaiqiang Ma, Hangzhou Dianzi Univ. (China)
Lingling Sun, Hangzhou Dianzi Univ. (China)
Yaqi Wang, Hangzhou Dianzi Univ. (China)
Junchao Wang, Hangzhou Dianzi Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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