## 2 Related Work
Firstly, the authors trained a deterministic U-Net to separate body parts from other objects in the image. Then, the Bayesian U-Net was used for segmentation and finding uncertainty maps based on the labeled dataset. Finally, the unlabeled dataset was segmented based on the trained model;if the segmentation uncertainty was high, then experts were asked to correct the automated segmentation; otherwise, the image was processed in the next iteration. [PERSON] et al. [4] introduced three uncertainty assessment approaches, including MC dropout, ensemble MC, and deep ensemble, to classify the skin cancer tissues. Each of those uncertainty measurements was tested with DenseNet201, MobileNetV2, ResNet152V2, and InceptionResNetV2 deep neural network architectures. Also, the authors proposed a model based on the Three-Way Decision (TWD) theory, which combines multiple uncertainty measurements and can be adjusted based on each dataset. The experiment used two binary skin cancer datasets, 20% and 50% dropout rates, and 50 samples per prediction. [PERSON] et al. [14] developed a BNN architecture called BayesNuSeg. The model was trained on the histopathology image datasets called PanNuke to perform a multi-class segmentation task. The BayesNuSeg model contained one encoder and two decoders. The encoder contained five convolutional layers, and the decoders generated a seed map and pixel embedding. The proposed architecture was compared with multiple Bayesian and deterministic models, and with 5-fold cross-validation, 50% dropout rate, and 500 epochs of training, the BayesNuSeg model achieved the highest IoU and F1-score. Unlike the above papers, the following studies investigated the influence of dropout rate for measuring uncertainty. [PERSON] et al. [15] used ultrasonography (US) and MRI datasets for segmenting knee femoral cartilage. Bayesian CNN with MC dropout was used to measure uncertainty and compared with a similar CNN architecture without MC dropout. The model was trained with a 10% and 50% dropout rate to evaluate the dropout rate on the model. Fifty samples per prediction were used to quantify uncertainty, and after 31 epochs, the Bayesian CNN achieved a higher Dice score for both MRI and US images compared to CNN without uncertainty assessment. [PERSON] et al. [16] introduced U-Net architecture for multi-class segmentation of the carotid artery MRI dataset. The goal of this work was to compare the performance and reliability of various uncertainty metrics. One of the experiments tests nine different dropout rates, including 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%. After 600 epochs of training, the model with a 30% dropout achieved 99%, 76%, and 85% Dice scores for background, vessel wall, and lumen, respectively. [PERSON] et al. [17] also investigated the MC dropout rate by testing Bayesian U-Net architecture with 10%, 20%, 30%, 40%, and 50% dropout rates. In addition to the fixed dropout rate, they also implemented a so-called \"concrete dropout\", which treats the dropout rate as a hyperparameter that can be optimized during the training. The performance of the Bayesian U-Net was compared with DenseNet, U-Net without uncertainty, and the ensemble model by combining DenseNet and U-Net. The models trained based on MRI images of the infant's brain after 100 epochs U-Net with concrete dropout achieve the highest result. [PERSON] et al. [2] proposed a framework called SkiNet, which segments lesions in images of skin cancer and then classified the lesions into predefined types. The dataset was imbalanced and contained seven classes of skin lesions; sample augmentation was performed to balance the dataset. Again, MC dropout was adapted to add uncertainty assessment to the U-Net model. The dropout rate from the 40% to 70% range was applied (without specifying the exact number), and the best result was achieved with a 50% dropout. [PERSON] et al. [3] proposed different techniques to measure uncertainty in two MRI cardiac datasets. The techniques were ensemble, Backprop, Stochastic Segmentation Networks, and MC dropout.