## 3 Methodology
### False-Positives Classification Module (FPC)  
Clinically, positive represents abnormal tissue or some disease in the human body and negative represents normal. False positive refers to a phenomenon in which one healthy (negative) person receives a positive symptom. In short, it is a disease-free misdiagnosis. False positives are common in medical detection. Since there are more pure negative samples in medical image data, the imbalance between positive and negative data is serious. Therefore, in most medical image organ segmentation tasks, false positive results (organs detected in no-organ images) are frequent occurrences. False positive results may lead to more serious follow-up medical accidents, resulting in a waste of hospital medical resources and psychological distress to the patient, thereby causing physical and mental harm to patients.  
In order to solve this problem and to achieve more accurate medical image segmentation, we specially designed a module (FPC) for screening false positive phenomena and added it to the proposed network model. Our goal is to determine whether there is an organ in the input image through this module and to predict the mask based on the screening results.  
The architecture of FPC is shown in Figure 5. In the Render3D U-Net architecture, the deepest block (\(X_{4\_0}\)) in the encoder owns the richest semantic information after hierarchical convolution operations. To make full use of it, we designed a pyramid structure to extract organ existence information in semantic features. This pyramid structure is composed of the following layers from bottom to top:  
Figure 4: Detailed analysis of the point-sampling method was applied to image semantic segmentation. A set of uncertain points from coarse prediction was selected to extract point-features. These features will replace original upsampling features and concatenate to refine the boundary.  
1. Dropout layer (by 0.5): this layer can effectively prevent the network training from overfitting.
2. Convolution layer and adaptive maxpool layer (by 2): these two layers can extract organ features from semantic information.
3. Sigmoid layer (\(sigmoid(x)=\frac{1}{1+e^{(-x)}}\)): this layer is a mathematical tool that can obtain the _probability_ value of binary classification (\(probability\in[0,1]\)), which represents the uncertainty of the organ existence in the input image.
4. Argmax layer: this layer can select the highest _probability_ value among \(\{0,1\}\), where 0 represents no existence of an organ and 1 represents the existence of an organ.  
Then, the FPC module dot products of the output image from the nodes in the first layer with the classification results \(\{0\}\) or \(\{1\}\):  
* If the previous step determines that there is an organ in the input image, the result of the dot product with 1 will still be the original output.
* Otherwise, the result of the dot product with 0 will be black, thereby reducing the possibility of misdiagnosis as a false positive.  
In essence, the FPC module is a binary classification task and then blackens the output mask of nonexistent organs according to the classification result. In order to train this module, we take advantage of the simplicity of the binary classification task and used binary cross entropy (BCE) as the loss function. This loss can help reduce the possibility of oversegmentation caused by false positives while preserving the original organ features to the greatest extent. Eventually, the impact of false positives is reduced by the false positive classification module.