Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey

• URL: http://arxiv.org/abs/2106.00997v1
• Date: Wed, 2 Jun 2021 07:46:02 GMT
• Title: Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey
• Authors: Changhee Han, Takayuki Okamoto, Koichi Takeuchi, Dimitris Katsios, Andrey Grushnikov, Masaaki Kobayashi, Antoine Choppin, Yutaka Kurashina, Yuki Shimahara
• Abstract summary: Convolutional Neural Networks (CNNs) intrinsically require large-scale data whereas Chest X-Ray (CXR) images tend to be data/annotation-scarce, leading to over-fitting. This paper thoroughly introduces tricks to improve generalization in the CXR diagnosis: how to (i) leverage additional data, (ii) augment/distillate data, (iii) regularize training, and (iv) conduct efficient segmentation.
• Score: 0.12647816797166167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks (CNNs) intrinsically requires large-scale data whereas Chest X-Ray (CXR) images tend to be data/annotation-scarce, leading to over-fitting. Therefore, based on our development experience and related work, this paper thoroughly introduces tricks to improve generalization in the CXR diagnosis: how to (i) leverage additional data, (ii) augment/distillate data, (iii) regularize training, and (iv) conduct efficient segmentation. As a development example based on such optimization techniques, we also feature LPIXEL's CNN-based CXR solution, EIRL Chest Nodule, which improved radiologists/non-radiologists' nodule detection sensitivity by 0.100/0.131, respectively, while maintaining specificity.

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