4 citations · 5 across the 13 of their papers we have counts for
14 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…
Cell Instance Segmentation: The Devil Is in the Boundaries
Peixian Liang, Yifan Ding, Yizhe Zhang +9
State-of-the-art (SOTA) methods for cell instance segmentation are based on deep learning (DL) semantic segmentation approaches, focusing on distinguishing foreground pixels from b…
TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classification
Pengfei Gu, Hongxiao Wang, Yejia Zhang +3
Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). % Despite remarkable su…
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…
Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation
Pengfei Gu, Huimin Li, Yejia Zhang +2
Masked Autoencoders (MAEs) have been shown to be effective in pre-training Vision Transformers (ViTs) for natural and medical image analysis problems. By reconstructing missing pix…
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…
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification
Yizhe Zhang, Shuo Wang, Yejia Zhang +1
Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP p…
SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings
Yejia Zhang, Pengfei Gu, Nishchal Sapkota +1
Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions. Although effective, thi…
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images
Yejia Zhang, Nishchal Sapkota, Pengfei Gu +3
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard. Given an image patch, its core spatial p…
ConvFormer: Combining CNN and Transformer for Medical Image Segmentation
Pengfei Gu, Yejia Zhang, Chaoli Wang +1
Convolutional neural network (CNN) based methods have achieved great successes in medical image segmentation, but their capability to learn global representations is still limited…
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation
Yejia Zhang, Xinrong Hu, Nishchal Sapkota +2
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations. The idea is to make…
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning
Yejia Zhang, Pengfei Gu, Nishchal Sapkota +3
High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer le…
Improving Human Sperm Head Morphology Classification with Unsupervised Anatomical Feature Distillation
Yejia Zhang, Jingjing Zhang, Xiaomin Zha +3
With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis. Recent deep learning (DL) morphology analysis method…