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20222024
most citedTouchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

2 citations · 5 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CV2024

Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation

Zipeng Qi, Hao Chen, Haotian Zhang +2

In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency. Our method projects the RGB attributes and semantic…

cs.CV20242 cited

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…

cs.CV20241 cited

Generative Active Learning for Long-tailed Instance Segmentation

Muzhi Zhu, Chengxiang Fan, Hao Chen +4

Recently, large-scale language-image generative models have gained widespread attention and many works have utilized generated data from these models to further enhance the perform…

cs.CV2024

Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation

Hao Chen, Hongrun Zhang, U Wang Chan +3

Single domain generalization aims to address the challenge of out-of-distribution generalization problem with only one source domain available. Feature distanglement is a classic s…

cs.CV20241 cited

FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Linshan Wu, Jiaxin Zhuang, Xuefeng Ni +1

AI-driven tumor analysis has garnered increasing attention in healthcare. However, its progress is significantly hindered by the lack of annotated tumor cases, which requires radio…

cs.CV2024

Learning to detect cloud and snow in remote sensing images from noisy labels

Zili Liu, Hao Chen, Wenyuan Li +5

Detecting clouds and snow in remote sensing images is an essential preprocessing task for remote sensing imagery. Previous works draw inspiration from semantic segmentation models…