1 citations · 1 across the 4 of their papers we have counts for
4 papers
Displacement Preserving Relational Distillation for Robust Medical Segmentation
Zhicheng Ding, Xinyu Chu, Jung Im Choi +5
Accurate 3D medical segmentation is limited by anatomical variability and high computational costs. While knowledge distillation (KD) offers a route for model compression, conventi…
Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation
Mengchen Fan, Baocheng Geng, Xi Xiao +5
Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefore necessary, but compact 3D…
From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows
Qizhen Lan, Aaron Choi, Jun Ma +4
Deploying medical image segmentation models in routine clinical workflows is often constrained by on-premises infrastructure, where computational resources are fixed and cloud-base…
ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation
Qizhen Lan, Yu-Chun Hsu, Nida Saddaf Khan +1
Accurate 3D medical image segmentation is vital for diagnosis and treatment planning, but state-of-the-art models are often too large for clinics with limited computing resources.…