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

no code implementations • 2 Jun 2021 • Changhee Han, Takayuki Okamoto, Koichi Takeuchi, Dimitris Katsios, Andrey Grushnikov, Masaaki Kobayashi, Antoine Choppin, Yutaka Kurashina, Yuki Shimahara

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.

Specificity

MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction

no code implementations • 24 Jul 2020 • Changhee Han, Leonardo Rundo, Kohei Murao, Tomoyuki Noguchi, Yuki Shimahara, Zoltan Adam Milacski, Saori Koshino, Evis Sala, Hideki Nakayama, Shinichi Satoh

Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices.

Generative Adversarial Network MRI Reconstruction +1