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20212026
most citedA Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

3 citations · 7 across the 10 of their papers we have counts for

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cs.CV2026

HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation

Yunhao Bai, Zhongwei Qiu, Guangyu Guo +5

Clinical intelligence requires estimating a patient's underlying condition from incomplete observations rather than learning isolated mappings from scans to answers. Volumetric med…

cs.CV2025

From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans

Qinji Yu, Yirui Wang, Ke Yan +11

Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging, treatment planning and beyond. Identifying scatte…

cs.CV2024★ 1 cited

RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

Yunhao Bai, Boxiang Yun, Zeli Chen +3

The Segment Anything Model 2 (SAM2) has recently demonstrated exceptional performance in zero-shot prompt segmentation for natural images and videos. However, when the propagation…

cs.CV2024

Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer

Yirui Wang, Qinji Yu, Ke Yan +9

Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for…

cs.CV2023

ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features

Jingqi Niu, Qinji Yu, Shiwen Dong +3

Detecting anomalies in fundus images through unsupervised methods is a challenging task due to the similarity between normal and abnormal tissues, as well as their indistinct bound…

cs.CV2023

Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

Qinji Yu, Nan Xi, Junsong Yuan +3

Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to transfer the knowledge learned from a labeled source domain to an unlabeled target domain…