activity
20192026
most citedCyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation

21 citations · 37 across the 17 of their papers we have counts for

collaborators

19 papers

cs.CV2026

UniH: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

Zhiwen Yang, Jiayin Li, Chengyu Liu +3

All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Existing methods typically priori…

cs.CV2026

U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training

Zhiwen Yang, Jiayin Li, Hao Lu +3

Existing deep learning models for Positron Emission Tomography (PET) image denoising often suffer from severe performance degradation under distribution shifts, fundamentally restr…

cs.CV2026

MedVol-R1: Reward-Driven Evidence Grounding for Volumetric Reasoning Segmentation

Zichun Wang, Hairong Shi, Bingzheng Wei +2

Volumetric Reasoning Segmentation (VRS) aims to segment a target region in a 3D medical scan from a free-form clinical query, where the referent is often implicit and requires both…

cs.CV2026

CTIS-QA: Clinical Template-Informed Slide-level Question Answering for Pathology

Hao Lu, Ziniu Qian, Yifu Li +3

In this paper, we introduce a clinical diagnosis template-based pipeline to systematically collect and structure pathological information. In collaboration with pathologists and gu…

cs.CV2025

TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration

Zhiwen Yang, Jiaju Zhang, Yang Yi +3

Medical image restoration (MedIR) aims to recover high-quality medical images from their low-quality counterparts. Recent advancements in MedIR have focused on All-in-One models ca…

cs.CV2025

All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior

Haowei Chen, Zhiwen Yang, Haotian Hou +4

All-in-one medical image restoration (MedIR) aims to address multiple MedIR tasks using a unified model, concurrently recovering various high-quality (HQ) medical images (e.g., MRI…

cs.CV2025

Visual Textualization for Image Prompted Object Detection

Yongjian Wu, Yang Zhou, Jiya Saiyin +2

We propose VisTex-OVLM, a novel image prompted object detection method that introduces visual textualization -- a process that projects a few visual exemplars into the text feature…

cs.CV2025

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation

Huihan Wang, Zhiwen Yang, Hui Zhang +3

Synthesizing high-quality dynamic medical videos remains a significant challenge due to the need for modeling both spatial consistency and temporal dynamics. Existing Transformer-b…

cs.CV2024★ 5 cited

AttriPrompter: Auto-Prompting with Attribute Semantics for Zero-shot Nuclei Detection via Visual-Language Pre-trained Models

Yongjian Wu, Yang Zhou, Jiya Saiyin +4

Large-scale visual-language pre-trained models (VLPMs) have demonstrated exceptional performance in downstream object detection through text prompts for natural scenes. However, th…

cs.CV2024

SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained Models

Yang Zhou, Yongjian Wu, Jiya Saiyin +4

Prompt tuning methods have achieved remarkable success in parameter-efficient fine-tuning on large pre-trained models. However, their application to dual-modal fusion-based visual-…

eess.IV2024

Region Attention Transformer for Medical Image Restoration

Zhiwen Yang, Haowei Chen, Ziniu Qian +5

Transformer-based methods have demonstrated impressive results in medical image restoration, attributed to the multi-head self-attention (MSA) mechanism in the spatial dimension. H…

eess.IV2024★ 2 cited

Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV

Zhiwen Yang, Jiayin Li, Hui Zhang +3

Transformers have revolutionized medical image restoration, but the quadratic complexity still poses limitations for their application to high-resolution medical images. The recent…

cs.CV2024

All-In-One Medical Image Restoration via Task-Adaptive Routing

Zhiwen Yang, Haowei Chen, Ziniu Qian +5

Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider applica…

eess.IV2023

DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis

Zhiwen Yang, Yang Zhou, Hui Zhang +3

Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. The generalizability of existing methods can…

cs.CV2023

Zero-shot Nuclei Detection via Visual-Language Pre-trained Models

Yongjian Wu, Yang Zhou, Jiya Saiyin +5

Large-scale visual-language pre-trained models (VLPM) have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection…

cs.CV2023★ 21 cited

Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation

Yang Zhou, Yongjian Wu, Zihua Wang +5

Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manua…

cs.CV2022

Transformer based multiple instance learning for weakly supervised histopathology image segmentation

Ziniu Qian, Kailu Li, Maode Lai +4

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates th…

cs.IR2021

Group based Personalized Search by Integrating Search Behaviour and Friend Network

Yujia Zhou, Zhicheng Dou, Bingzheng Wei +1

The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were pro…

cs.SI2019★ 9 cited

Popularity Prediction on Social Platforms with Coupled Graph Neural Networks

Qi Cao, Huawei Shen, Jinhua Gao +2

Predicting the popularity of online content on social platforms is an important task for both researchers and practitioners. Previous methods mainly leverage demographics, temporal…