activity
20222025
most citedSkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model

44 citations · 122 across the 12 of their papers we have counts for

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Showing eess.IVShow all

5 papers · 1 filter

eess.IV202310 cited

Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model

Shaoyan Pan, Elham Abouei, Jacob Wynne +10

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone…

eess.IV20238 cited

BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images

Mingzhe Hu, Yuheng Li, Xiaofeng Yang

Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic too…

eess.IV202315 cited

Polyp-SAM: Transfer SAM for Polyp Segmentation

Yuheng Li, Mingzhe Hu, Xiaofeng Yang

Colon polyps are considered important precursors for colorectal cancer. Automatic segmentation of colon polyps can significantly reduce the misdiagnosis of colon cancer and improve…

eess.IV202318 cited

Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis

Shaoyan Pan, Chih-Wei Chang, Junbo Peng +7

This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each…

eess.IV20231 cited

Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation

Shaoyan Pan, Shao-Yuan Lo, Min Huang +5

In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR)…