## 1 Introduction
The boundary is separately learned as a binary classification problem. The introduction of the so-called skip-connection detects the boundary more accurately [21]. The recurrent neural network-based methods rely on LSTMs [22] and an advanced form of Gated Recurrent Units. In [23], a Multi-Dimensional Recurrent Unit was proposed for brain tumor segmentation. The RNN was combined with conditional random fields for post-processing [24]. Generative adversarial networks run in the same manner as a min-max game. The generator network generates an artificial segmentation, while the discriminator network finds its differences compared to the ground truth. If the generator is able to generate segmentation very close to the ground truth, the network-pair is considered trained [25]. Another network based on the GA technique is the SegAN [26]. In this article, the segmentor network is an FCN network. The discriminator is trained with a multiscale L1 loss function by maximizing it, and the segmentor only uses the gradients of the critic. The BraTS Challenge has been organized since 2012 and continues today [4; 5; 6]. Many researchers in the field participated in different editions. In the first few years, i.e., 2012-2014, generative, discriminative, or their combinations were proposed. The best methods integrated a hierarchical random forest classifier [27] or context-sensitive feature extraction with decision tree [28]. Until 2016, different versions of the random forest [9] classifier were in the top three methods [29]. In the 2015 BraTS, the simple convolutional neural networks appeared. In [30], a network similar to LeNet-5 for brain tumor segmentation was proposed. However, their Dice scores on the whole tumor (WT = 0.81) was slightly smaller than the leaders' [31] using random forest classification (WT = 0.84). The best results reported in 2016 were obtained by a 5-layered simple convnet reporting Dice scores of the Whole Tumor \(-\) WT = 0.87, Tumor Core \(-\) TC = 0.81, and Enhanced Tumor \(-\) ET = 0.72 [32]. In 2017, [PERSON] et al. [25] proposed an ensemble of multiple architectures known from the literature and obtained the best results (WT = 0.90, TC = 0.82, ET = 0.79). The NVIDIAcompany was the winning team in 2018 using multiple Tesla V100 GPUs [33] and applying an autoencoder-decoder CNN architecture (WT = 0.91, TC = 0.86, ET = 0.82). In 2019, a variant of cascaded versions of UNet obtained the best results combining 12 different CNNs into an ensemble model (WT = 0.89, TC = 0.83, ET = 0.83) [34]. The most up-to-date and best brain tumor segmentation networks were presented recently at the 2020 edition of the BRATS Challenge. Last year's third place research team presented an encoder-decoder architecture called SA-Net [35]. [PERSON] et al. [36] and [PERSON] et al. [37] both occupied rank 2. In [36], the authors used a modality pairing procedure instead of using all four modalities at the same time. The pairs of modalities fed into the two different branches are T1 with T1c and T2 with FLAIR, respectively. The architecture is the same 3D UNet presented in [13]. In the other paper, which ranked second [37], the authors propose a Hybrid-High-Resolution and Non-local Feature network. [PERSON] proposed in [38; 39] the so-called nnUNET architecture--an autonomous system that computes hyperparameters and ties three architectures (2D, 3D, 3D-cascade UNet) based on the vanilla UNet using k-fold cross-validation deep supervision learning and ensemble architecture. This application was put into practice and confirmed on several challenges in the medical field last year. It is based on a dataset fingerprint and a pipeline fingerprint. The dataset fingerprint determines the resampling, intensity normalization, standardization, image sizes, cropping, and class ratio. The pipeline fingerprint is separated into three groups: blueprint, inferred, and empirical. The inference is made via a sliding window approach using half overlapping adjacent patches.