output
20172022
most citedFederated Learning Enables Big Data for Rare Cancer Boundary Detection

390 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

cs.LG2022★ 390 cited

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Sarthak Pati, Ujjwal Baid, Brandon Edwards +276

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…

eess.IV2021★ 16 cited

The Federated Tumor Segmentation (FeTS) Challenge

Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…

q-bio.BM2020★ 13 cited

Pain relief devoid of opioid side effects following central action of a silylated neurotensin analog

Pascal Tétreault, Élie Besserer-Offroy, Rebecca L. Brouillette +12

Neurotensin (NT) exerts naloxone-insensitive antinociceptive action through its binding to both NTS1 and NTS2 receptors and NT analogs provide stronger pain relief than morphine on…

eess.SP2018★ 7 cited

Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir

Marc-Antoine Moinnereau, Thomas Brienne, Simon Brodeur +3

The use of electroencephalogram (EEG) as the main input signal in brain-machine interfaces has been widely proposed due to the non-invasive nature of the EEG. Here we are specifica…

cs.CV2017

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin +9

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk…