Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images

By Changhee Han, K. Murao, T. Noguchi, Yusuke Kawata, F. Uchiyama, Leonardo Rundo, Hideki Nakayama, S. Satoh. 2019

102 citations recorded by the source corpus.

Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize additional training data to handle the small/fragmented medical imaging datasets collected from various scanners; those images are realistic but completely different from the original ones, filling the data lack in the real image distribution. However, we cannot easily use them to locate disease areas, considering expert physicians' expensive annotation cost. Therefore, this paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 × 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic bounding box annotation improves the training robustness. The results show that CPGGAN-based DA can boost 10% sensitivity in diagnosis with clinically acceptable additional False Positives. Surprisingly, further tumor realism, achieved with additional normal brain MR images for CPGGAN training, does not contribute to detection performance, while even three physicians cannot accurately distinguish them from the real ones in Visual Turing Test.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lawrence Carin
2. Jianqiao Li
3. Sijia Wang
4. Yulai Cong
5. Miaoyun Zhao
6. Chunyuan Li
7. Qiang Yang
8. Yu Zhang

In Computer Vision and Pattern Recognition

1. Lawrence Carin
2. Jianqiao Li
3. Sijia Wang
4. Yulai Cong
5. Miaoyun Zhao
6. Chunyuan Li
7. Zhe Gan

In Radiology, Nuclear Medicine and Imaging

1. Lawrence Carin
2. Yulai Cong
3. Miaoyun Zhao

Citations in the corpus

• GAN Memory with No Forgetting (2020, background): “GAN [25] setup as the key component of that generative memory, which we term GAN memory, because (i) GANs have shown remarkable power in synthesizing realistic high-dimensional samples [9, 51, 33, 34]; (ii) by modeling the generative process of training data, GANs summarize the data statistical info”