37 citations · 101 across the 13 of their papers we have counts for
14 papers
UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning
Xintian Mao, Jiansheng Wang, Huaiyuan Wang +2
Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurring performance of these models dr…
Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring
Shaoqing Duan, Haofei Song, Xintian Mao +2
Defocus deblurring in pathological microscopy remains challenging due to the spatially varying and locally discontinuous nature of optical blur induced by a position-dependent inte…
DeepRFTv2: Kernel-level Learning for Image Deblurring
Xintian Mao, Haofei Song, Yin-Nian Liu +2
It is well-known that if a network aims to learn how to deblur, it should understand the blur process. Blurring is naturally caused by the convolution of the sharp image with the b…
LoFormer: Local Frequency Transformer for Image Deblurring
Xintian Mao, Jiansheng Wang, Xingran Xie +2
Due to the computational complexity of self-attention (SA), prevalent techniques for image deblurring often resort to either adopting localized SA or employing coarse-grained globa…
Spatially-Variant Degradation Model for Dataset-free Super-resolution
Shaojie Guo, Haofei Song, Qingli Li +1
This paper focuses on the dataset-free Blind Image Super-Resolution (BISR). Unlike existing dataset-free BISR methods that focus on obtaining a degradation kernel for the entire im…
Prompting Whole Slide Image Based Genetic Biomarker Prediction
Ling Zhang, Boxiang Yun, Xingran Xie +3
Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide im…
AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring
Xintian Mao, Qingli Li, Yan Wang
Despite the recent progress in enhancing the efficacy of image deblurring, the limited decoding capability constrains the upper limit of State-Of-The-Art (SOTA) methods. This paper…
INet: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis
Haofei Song, Xintian Mao, Jing Yu +2
Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low t…
Gene-induced Multimodal Pre-training for Image-omic Classification
Ting Jin, Xingran Xie, Renjie Wan +2
Histology analysis of the tumor micro-environment integrated with genomic assays is the gold standard for most cancers in modern medicine. This paper proposes a Gene-induced Multim…
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
Yunhao Bai, Duowen Chen, Qingli Li +2
In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data…
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery
Duowen Chen, Yunhao Bai, Wei Shen +3
We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regulariz…
SR: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification
Xingran Xie, Yan Wang, Qingli Li
Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational…
Intriguing Findings of Frequency Selection for Image Deblurring
Xintian Mao, Yiming Liu, Fengze Liu +3
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always desig…
SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
Boxiang Yun, Yan Wang, Jieneng Chen +3
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and…