Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategies

arXiv:2404.09761v1 Announce Type: cross
Abstract: Cancer is one of the leading causes of death globally, and early diagnosis is crucial for patient survival. Deep learning algorithms have great potential for automatic cancer analysis. Artificial intelligence has achieved high performance in recognizing and segmenting single lesions. However, diagnosing multiple lesions remains a challenge. This study examines and compares various neural network architectures and training strategies for automatically segmentation of cancer lesions using PET/CT images from the head, neck, and whole body. …

abstract algorithms analysis architectures artificial artificial intelligence arxiv benchmark cancer cs.cv cs.lg death deep learning deep learning algorithms diagnosis eess.iv intelligence patient performance pet segmentation strategies survival training tumors type