activity
20182021
most citedStereo Correspondence and Reconstruction of Endoscopic Data Challenge

24 citations · 27 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV202124 cited

Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

Max Allan, Jonathan Mcleod, Congcong Wang +21

The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform…

cs.CV2020

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…

cs.CV20192 cited

Adaptive Context Encoding Module for Semantic Segmentation

Congcong Wang, Faouzi Alaya Cheikh, Azeddine Beghdadi +1

The object sizes in images are diverse, therefore, capturing multiple scale context information is essential for semantic segmentation. Existing context aggregation methods such as…

cs.CV2019

Generative Smoke Removal

Oleksii Sidorov, Congcong Wang, Faouzi Alaya Cheikh

In minimally invasive surgery, the use of tissue dissection tools causes smoke, which inevitably degrades the image quality. This could reduce the visibility of the operation field…

cs.CV20181 cited

Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?

Congcong Wang, Vivek Sharma, Yu Fan +4

Laparoscopic surgery has a limited field of view. Laser ablation in a laproscopic surgery causes smoke, which inevitably influences the surgeon's visibility. Therefore, it is of vi…

cs.CV2018

A Smoke Removal Method for Laparoscopic Images

Congcong Wang, Faouzi Alaya Cheikh, Mounir Kaaniche +1

In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces error for the image processing (used in image guided surgery), but also…