69 citations · 207 across the 11 of their papers we have counts for
11 papers
Mixed Transformer U-Net For Medical Image Segmentation
Hongyi Wang, Shiao Xie, Lanfen Lin +4
Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers h…
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
Yue Zhang, Chengtao Peng, Liying Peng +8
Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usuall…
Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer
Panyanat Aonpong, Yutaro Iwamoto, Xian-Hua Han +2
Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate after the surgery. Recently, many machine learning methods have been proposed for recurrence…
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Huimin Huang, Ming Cai, Lanfen Lin +9
Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed fo…
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
Yingying Xu, Ming Cai, Lanfen Lin +10
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is new…
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data
Yinhao Li, Yutaro Iwamoto, Lanfen Lin +2
Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional…
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors
Yutaro Iwamoto, Kyohei Takeda, Yinhao Li +2
Deep learning techniques have led to state-of-the-art image super resolution with natural images. Normally, pairs of high-resolution and low-resolution images are used to train the…
Interactive Deep Refinement Network for Medical Image Segmentation
Titinunt Kitrungrotsakul, Iwamoto Yutaro, Lanfen Lin +3
Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation. The techniques have also been applied to medical image segm…
Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
Rui Xu, Tiantian Liu, Xinchen Ye +1
Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important fo…
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
Huimin Huang, Lanfen Lin, Ruofeng Tong +6
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widel…
A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images
Titinunt Kitrungrotsakul, Xian-Hau Han, Yutaro Iwamoto +6
In recent years, intravital skin imaging has been increasingly used in mammalian skin research to investigate cell behaviors. A fundamental step of the investigation is mitotic cel…