Soft Robotic Bladder
Soft robotic bladders are being developed as bio-inspired models for studying and treating urinary and fecal incontinence, as well as for creating novel haptic interfaces. Current research focuses on creating accurate biomimetic models using soft actuators and advanced control systems, often incorporating machine learning techniques like convolutional neural networks and multi-layer perceptrons for improved performance and data analysis. These advancements are improving our understanding of bladder and rectal function, enabling more precise diagnosis of conditions like bladder cancer and potentially leading to the development of improved therapeutic devices and assistive technologies.
Papers
Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation
Deepak Raina, Kashish Verma, SH Chandrashekhara, Subir Kumar Saha
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network
Yu Ren, Guoli Wang, Pingping Wang, Kunmeng Liu, Quanjin Liu, Hongfu Sun, Xiang Li, Benzheng Wei