ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation

9 September 2023

Papers citing "ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation"

9 / 9 papers shown

Title
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation Lexin Fang Yunyang Xu Xiang Ma Xuemei Li Caiming Zhang 51 0 0 14 Mar 2025
PARF-Net: integrating pixel-wise adaptive receptive fields into hybrid Transformer-CNN network for medical image segmentation Xu Ma Mengsheng Chen Junhui Zhang Lijuan Song Fang Du Zhenhua Yu ViT MedIm 39 0 0 06 Jan 2025
Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation Qian Chen Lei Zhu Hangzhou He Xinliang Zhang Shuang Zeng Qiushi Ren Yanye Lu CLL 45 1 0 19 Jun 2024
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation Md Mostafijur Rahman Mustafa Munir R. Marculescu MedIm 34 34 0 11 May 2024
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 322 7,503 0 11 Nov 2021
MISSFormer: An Effective Medical Image Segmentation Transformer Xiaohong Huang Zhifang Deng Dandan Li Xueguang Yuan ViT MedIm 98 174 0 15 Sep 2021
TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation Bingzhi Chen Yishu Liu Zheng-Wei Zhang Guangming Lu A. W. Kong MedIm ViT 109 209 0 12 Jul 2021
Convolution-Free Medical Image Segmentation using Transformers Davood Karimi Serge Vasylechko Ali Gholipour ViT MedIm 84 121 0 26 Feb 2021
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation Yundong Zhang Huiye Liu Qiang Hu ViT MedIm 206 897 0 16 Feb 2021