activity
20172024
most citedBi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks

72 citations · 121 across the 21 of their papers we have counts for

collaborators

21 papers

cs.LG2024

Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers

Pingcheng Dong, Yonghao Tan, Dong Zhang +11

Non-linear functions are prevalent in Transformers and their lightweight variants, incurring substantial and frequently underestimated hardware costs. Previous state-of-the-art wor…

cs.CV2024

SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts

Xian Lin, Yangyang Xiang, Zhehao Wang +3

Segment anything model (SAM), a foundation model with superior versatility and generalization across diverse segmentation tasks, has attracted widespread attention in medical imagi…

cs.CV2024

Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification

Shuhan Li, Yi Lin, Hao Chen +1

Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and…

cs.CV2024

BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels

Yi Lin, Zeyu Wang, Dong Zhang +2

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of t…

cs.ET2023

Pruning random resistive memory for optimizing analogue AI

Yi Li, Songqi Wang, Yaping Zhao +17

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unpreceden…

eess.IV202372 cited

Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks

Xin Yang, Yi Lin, Zhiwei Wang +2

In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consist…