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20222026
most citedFederated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

71 citations · 94 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis

Yuxuan Ou, Konstantinos Kamnitsas, OxAAA Study +3

Diffusion models can synthesise contrast-enhanced CT (CECT) from non-contrast CT (NCCT), avoiding contrast administration and its environmental and patient-access costs. However, v…

cs.CV2026

GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI

Julia Alekseenko, Pietro Mascagni, AI4SafeChole Consortium +1

Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices - selecting the "best" glob…

cs.CV2023

Jumpstarting Surgical Computer Vision

Deepak Alapatt, Aditya Murali, Vinkle Srivastav +3

Consensus amongst researchers and industry points to a lack of large, representative annotated datasets as the biggest obstacle to progress in the field of surgical data science. A…

cs.CV2023★ 23 cited

Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos

Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni +3

Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates educatio…

cs.CV2022★ 71 cited

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Hasan Kassem, Deepak Alapatt, Pietro Mascagni +3

Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such meth…