MedQ: Lossless ultra-low-bit neural network quantization for medical image segmentation

Author:

Zhang Rongzhao,Chung Albert C.S.

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference67 articles.

1. Ternary neural networks for resource-efficient ai applications;Alemdar,2017

2. U-net fixed-point quantization for medical image segmentation;AskariHemmat,2019

3. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge;Bakas,2018

4. Bengio, Y., Léonard, N., Courville, A., 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432.

5. Bethge, J., Bartz, C., Yang, H., Chen, Y., Meinel, C., 2020. MeliusNet: can binary neural networks achieve mobilenet-level accuracy? arXiv preprint arXiv:2001.05936.

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