11 citations · 20 across the 9 of their papers we have counts for
9 papers
Robust Learning Protocol for Federated Tumor Segmentation Challenge
Ambrish Rawat, Giulio Zizzo, Swanand Kadhe +2
In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enab…
Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization
Jonathan P. Epperlein, Niall P. Hardy, Pol Mac Aonghusa +1
Intraoperative assessment of tissue can be guided through fluorescence imaging which involves systemic dosing with a fluorophore and subsequent examination of the tissue region of…
A Real-Time Region Tracking Algorithm Tailored to Endoscopic Video with Open-Source Implementation
Jonathan P. Epperlein, Sergiy Zhuk
With a video data source, such as multispectral video acquired during administration of fluorescent tracers, extraction of time-resolved data typically requires the compensation of…
Reinforcement Learning with Algorithms from Probabilistic Structure Estimation
Jonathan P. Epperlein, Roman Overko, Sergiy Zhuk +4
Reinforcement learning (RL) algorithms aim to learn optimal decisions in unknown environments through experience of taking actions and observing the rewards gained. In some cases,…
Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures
Sergiy Zhuk, Jonathan P. Epperlein, Rahul Nair +4
Intra-operative identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method…
Bayesian Classifier for Route Prediction with Markov Chains
Jonathan P. Epperlein, Julien Monteil, Mingming Liu +3
We present here a general framework and a specific algorithm for predicting the destination, route, or more generally a pattern, of an ongoing journey, building on the recent work…
The Use of Presence Data in Modelling Demand for Transportation
Jonathan Epperlein, Jaroslaw Legierski, Marcin Luckner +2
We consider the applicability of the data from operators of cellular systems to modelling demand for transportation. While individual-level data may contain precise paths of moveme…
Recovering Markov Models from Closed-Loop Data
Jonathan P. Epperlein, Robert Shorten, Sergiy Zhuk
Situations in which recommender systems are used to augument decision making are becoming prevalent in many application domains. Almost always, these prediction tools (recommenders…
Resource Allocation with Population Dynamics
Jonathan Epperlein, Jakub Marecek
Many analyses of resource-allocation problems employ simplistic models of the population. Using the example of a resource-allocation problem of Marecek et al. [arXiv:1406.7639], we…