papers

Publications (8)

eess.IV2022

Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation

Kaisar Kushibar, Víctor Manuel Campello, Lidia Garrucho Moras +3

Uncertainty estimation in deep learning has become a leading research field in medical image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Mo…

eess.IV2022

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Richard Osuala, Kaisar Kushibar, Lidia Garrucho +6

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inte…

cs.LG2021

DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling

Akis Linardos, Matthias Kümmerer, Ori Press +1

Since 2014 transfer learning has become the key driver for the improvement of spatial saliency prediction; however, with stagnant progress in the last 3-5 years. We conduct a large…

cs.CV2025

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis

Florian Kofler, Marcel Rosier, Mehdi Astaraki +26

BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provi…

eess.IV2025

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

Florian Kofler, Marcel Rosier, Mehdi Astaraki +34

The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…

cs.CV2023

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…