9 papers · 1 filter
H-CNN-ViT: A Hierarchical Gated Attention Multi-Branch Model for Bladder Cancer Recurrence Prediction
Xueyang Li, Zongren Wang, Yuliang Zhang +6
Bladder cancer is one of the most prevalent malignancies worldwide, with a recurrence rate of up to 78%, necessitating accurate post-operative monitoring for effective patient mana…
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…
Boosting Medical Image Classification with Segmentation Foundation Model
Pengfei Gu, Zihan Zhao, Hongxiao Wang +5
The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images. Recently, SAM has gained a great deal of attention for its applicati…
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…
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…
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…