Alessandro Casella

TL;DR: An adversarial network consisting of two Fully-Convolutional Neural Networks that could be a valuable and robust solution to assist surgeons in providing membrane identification while performing fetoscopy.

TL;DR: In this paper, a new deep learning framework for inter-fetal membrane segmentation on in-vivo fetoscopic videos is presented, which enhances existing architectures by encoding a novel (instance-normalized) dense block, invariant to illumination changes, that extracts spatio-temporal features to enforce pixel connectivity in time, and relying on an adversarial training, which constrains macro appearance.

TL;DR: In this paper , only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%), while 48% of respondents applied postprocessing steps.

TL;DR: In this paper, the authors proposed a new approach based on adversarial Fully Convolutional Neural Networks (FCNNs) to kidney vessel segmentation from nephrectomy laparoscopic vision.

TL;DR: The authors performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021 and found that 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem.