6 citations · 11 across the 6 of their papers we have counts for
6 papers · 1 filter
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Yizhe Zhang, Tao Zhou, Shuo Wang +3
The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image seg…
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…