10 citations · 24 across the 11 of their papers we have counts for
11 papers
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…
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…
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Yizhe Zhang, Tao Zhou, Shuo Wang +2
The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1…
GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis
Yizhe Zhang, Danny Z. Chen
In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image…
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…
Data-Driven Deep Supervision for Skin Lesion Classification
Suraj Mishra, Yizhe Zhang, Li Zhang +3
Automatic classification of pigmented, non-pigmented, and depigmented non-melanocytic skin lesions have garnered lots of attention in recent years. However, imaging variations in s…
Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network
Yuexin Bian, Jintai Chen, Xiaojun Chen +3
A large number of people suffer from life-threatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of s…