260 citations · 446 across the 5 of their papers we have counts for
5 papers
Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge
Jun Ma, Yao Zhang, Song Gu +7
Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are…
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
Jun Ma, Yao Zhang, Song Gu +26
Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automa…
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
Jun Ma, Ronald Xie, Shamini Ayyadhury +37
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or req…
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
Jun Ma, Yao Zhang, Song Gu +14
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved…
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Jun Ma, Yixin Wang, Xingle An +10
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based…