# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 4 Experiments and Results  
### Data and Implementation Details  
We experiment with the publicly available medical dataset \"Chaos\" to justify the performance of our hybrid robust framework. The Chaos challenges have been held in The IEEE international Symposium on Biomedical Imaging (ISBI) 2019 and contain CT and MRI datasets. More details about the original data descriptions and data acquisition can be found in [43]. We choose CT data for liver segmentation and MRI data for abdominal multi-organ segmentation (i.e., liver, right kidney, left kidney and spleen). Each volume in these two datasets corresponds to a series of DICOM images belonging to a single patient. The CT dataset consists of 20 different patients and is divided into training (12 patients) andtesting (8 patients) in experiments. For the MRI dataset, we choose T2-SPIR (20 patients). We divide the 12 cases for training and the remaining 8 cases for testing. We resize the CT images as \(64\times 196\times 196\) and resize the MRI images as \(32\times 196\times 196\). We clip all values larger than 2000 as 2000, and then pre-process the data by normalization.  
As both the CT dataset and the MRI dataset have clean labels, we first generate noisy labeled images under different noise settings. Following the previous works of [14; 15], we set the number of slices selected for noise generation as the noise rate, and set the morphological changes within pixels for every selected slice as the noise level. For example, we set the noise rate to 50% and noise level to 1-8 pixels, which means we randomly select 50% of the slices from the whole volume and further erode or dilate every selected slice with 1-8 pixels.  
We utilize PyTorch for realizing our experiments. The learning rate is set empirically to 0.001. The Adam optimizer is used for optimization and the Betas of the optimizer are set to 0.9 and 0.999. We set the maximum epoch as 10,000 in the experiments. We repeat the experiments three times and report the average for the final performance. During training, we select 3D SE ResUnet as the backbone with four residual and SE blocks. The backbone consists of a contracting path and an expanding path. We prefer the contracting path as the LQAM module. During training, the batch size for the 3D segmentation model is set to 1, which means we obtain all predictions belonging to a patient in one shot. We use the widely used metric of dice score to measure the segmentation performance. \(\lambda\) is set to 0.01 for the experiments.  
As for the network architecture, we use 3D Unet as the basic backbone, with residual blocks and SE blocks. We expand the 2D squeeze, excitation, scale, and convolutional functions to obtain the 3D SE counterparts. The encoder consists of four residual modules with 16, 32, 32, and 64 output channels, followed by a down-sampling layer. The decoder shares the symmetric structure, but with up-sampling layers. For each residual module, we use two \(3\times 3\times 3\) convolution blocks, two SE modules, and corresponding up-sampling or down-sampling layers. The down-sampling layer is achieved via 3D max-pooling with stride 2 and the up-sampling is achieved with the strategy of nearest neighbor. Each convolution block consists of convolutional layers, an ELU activation function, and a 3D batch-normalization layer. We prefer the contracting path as the LQAM module.