value of the augmen- parameters randomly within certain ranges in variation. We tuned all ranges manually amination. In particular, we used a scaling n [0.8, 1.2], elastic deformation parameters and 2 [9.0, 11.0], additive Gaussian noise 0.1], Gaussian blurring with 2 [0, 0.1], ensity ratio between [0.65, 1.35], and con- y ratio between [0.5, 1.5]. For HSV-light ng, we used hue and saturation intensity ra- [ 0.1, 0.1] and [ 1, 1], respectively. For d HED-strong, we used intensity ratios be- , 0.05] and [ 0.2, 0.2], respectively, for all s. or normalization Figure 4: Network-based stain color normalization. From left to right: patches from the training set are transformed with heavy color augmen- tation and fed to a neural network. This network is trained to reconstruct the original appearance of the input images by removing color augmen- tation, e↵ectively learning how to perform stain color normalization. alize to unseen stains in order to perform well. We eval- uated several methods that implement g (see Fig. 3), and propose a novel technique based on neural networks. Identity. We performed no transformation on the in- put patches, serving as a baseline method for the rest of techniques. Stain color normalization 1. Identity: 何もしない 2. Grayscale: RGB to grayscale - ⾊情報を除く (augmentationはbasic, morphology, BCのみ) 3. LUT-based 核を検出して⾊の標準化 テンプレWSIからlook-up table (LUT)を作成する 4. Network-based(右図) Downward 5 layers, BN, LRA Upward 5 layers, nearest-neighbor upsampling + 1 conv BN, LRA+tanh, 64-sample mini-batch 4臓器のWSIから 500Kパッチ集めて使⽤ HSV augmentation (color transformation only) HSV value channel ratios b/w [-1, 1]