Search Results for author:
Found 5 papers, 4 papers with code
GuideGen: A Text-guided Framework for Joint CT Volume and Anatomical structure Generation
Secondly, our Conditional Image Generator autoregressively generates CT slices conditioned on a corresponding mask slice to incorporate both style information and anatomical guidance.
ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting
The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training.
AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge
When delineating lesions from medical images, a human expert can always keep in mind the anatomical structure behind the voxels.
A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation
In this work, we attempt to address the pixel-wise error map prediction problem and the per-case mask quality assessment problem using a unified deep learning (DL) framework.
A Unified Mammogram Analysis Method via Hybrid Deep Supervision
In this work, we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation.
