output
20222025
most citedBiomedical image analysis competitions: The state of current participation practice

17 citations

6 papers

cs.CV2025

Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios

Dhruv Jain, Romain Modzelewski, Romain Herault +3

In data-scarce scenarios, deep learning models often overfit to noise and irrelevant patterns, which limits their ability to generalize to unseen samples. To address these challeng…

cs.CV2025

Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics

Zacharia Mesbah, Dhruv Jain, Tsiry Mayet +5

The primary objective of the AIIB 2023 competition is to evaluate the predictive significance of airway-related imaging biomarkers in determining the survival outcomes of patients…

cs.LG2025★ 2 cited

Universal Domain Adaptation Benchmark for Time Series Data Representation

Romain Mussard, Fannia Pacheco, Maxime Berar +2

Deep learning models have significantly improved the ability to detect novelties in time series (TS) data. This success is attributed to their strong representation capabilities. H…

cs.CV2024

MiSuRe is all you need to explain your image segmentation

Syed Nouman Hasany, Fabrice Mériaudeau, Caroline Petitjean

The last decade of computer vision has been dominated by Deep Learning architectures, thanks to their unparalleled success. Their performance, however, often comes at the cost of e…

q-bio.NC2023★ 16 cited

Auditory cortex and beyond: Deficits in congenital amusia

Barbara Tillmann, Jackson Graves, Francesca Talamini +7

Congenital amusia is a neuro-developmental disorder of music perception and production, with the observed deficits contrasting with the sophisticated music processing reported for…

cs.CV2022★ 17 cited

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann, Annika Reinke, Vivienn Weru +352

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…