Segmentation Model
Segmentation models aim to partition images into meaningful regions, a crucial task across diverse fields like medical imaging and autonomous driving. Current research emphasizes improving model robustness and efficiency, focusing on architectures like U-Nets, Transformers, and diffusion models, often incorporating techniques like continual learning and prompt engineering to adapt to new data or tasks with minimal retraining. These advancements are driving improvements in accuracy and reducing the need for extensive labeled datasets, leading to wider applicability in various scientific and industrial applications.
Papers
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, Zongwei Zhou
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
Dennis Eschweiler, Rüveyda Yilmaz, Matisse Baumann, Ina Laube, Rijo Roy, Abin Jose, Daniel Brückner, Johannes Stegmaier
Learning Confident Classifiers in the Presence of Label Noise
Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii, Artem Agafonov, Mohammad Yaqub, Maxim Panov, Martin Takáč
Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation
Jinxin Lv, Xiaoyu Zeng, Sheng Wang, Ran Duan, Zhiwei Wang, Qiang Li
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro