Sigmoid Loss

Sigmoid loss is a training objective function increasingly used in deep learning models for image analysis, particularly within the context of contrastive learning and medical image segmentation. Current research focuses on its application in various architectures, including 3D U-Nets and generative adversarial networks (GANs), to improve the accuracy and efficiency of tasks such as colon segmentation from CT scans and H&E images, and 3D reconstruction of the colon from endoscopic video. This work is significant for advancing medical image analysis, enabling more accurate diagnosis and treatment planning for colorectal cancer and other gastrointestinal diseases, and improving the efficiency of pre-training large language-image models.