5 papers
When Swin Transformer Meets KANs: An Improved Transformer Architecture for Medical Image Segmentation
Nishchal Sapkota, Haoyan Shi, Yejia Zhang +5
Medical image segmentation is critical for accurate diagnostics and treatment planning, but remains challenging due to complex anatomical structures and limited annotated training…
UniCoN: Universal Conditional Networks for Multi-Age Embryonic Cartilage Segmentation with Sparsely Annotated Data
Nishchal Sapkota, Yejia Zhang, Zihao Zhao +10
Osteochondrodysplasia, affecting 2-3% of newborns globally, is a group of bone and cartilage disorders that often result in head malformations, contributing to childhood morbidity…
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
Hongxiao Wang, Yang Yang, Zhuo Zhao +3
For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, a…
SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
Nishchal Sapkota, Yejia Zhang, Sirui Li +7
Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer v…
ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentation
Nishchal Sapkota, Yejia Zhang, Susan M. Motch Perrine +8
Studying the morphological development of cartilaginous and osseous structures is critical to the early detection of life-threatening skeletal dysmorphology. Embryonic cartilage un…