3 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Deterministic Medical Image Translation via High-fidelity Brownian Bridges
Qisheng He, Nicholas Summerfield, Peiyong Wang +2
Recent studies have shown that diffusion models produce superior synthetic images when compared to Generative Adversarial Networks (GANs). However, their outputs are often non-dete…
Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
Behzad Hejrati, Soumyanil Banerjee, Carri Glide-Hurst +1
Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention…
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
Qisheng He, Nicholas Summerfield, Ming Dong +1
Medical image segmentation of tumors and organs at risk is a time-consuming yet critical process in the clinic that utilizes multi-modality imaging (e.g, different acquisitions, da…
Volumetric medical image segmentation through dual self-distillation in U-shaped networks
Soumyanil Banerjee, Nicholas Summerfield, Ming Dong +1
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in…
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
Hajar Emami, Ming Dong, Siamak Nejad-Davarani +1
In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by in…
Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer
Hajar Emami, Ming Dong, Carri K. Glide-Hurst
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that co…