activity
20182026
most citedTADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease

100 citations · 233 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data

Sterre de Jonge, Elisabeth J. Vinke, Meike W. Vernooij +3

Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understan…

eess.IV2022

Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors

Shahab Aslani, Watjana Lilaonitkul, Vaishnavi Gnanananthan +7

During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions.…

q-bio.PE2020

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…

q-bio.PE2020★ 73 cited

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +8

The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train…

cs.CV2019★ 60 cited

DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders

Razvan V. Marinescu, Arman Eshaghi, Marco Lorenzi +5

Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-te…

cs.LG2019

Disease Knowledge Transfer across Neurodegenerative Diseases

Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg +8

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal bio…