1.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Politecnico di MilanoIT15 papers
- Otto-von-Guericke-Universität MagdeburgDE6 papers
- Center for Behavioral Brain SciencesDE5 papers
- German Center for Neurodegenerative DiseasesDE4 papers
- European Bioinformatics InstituteGB3 papers
- Heidelberg UniversityDE3 papers
- Humanitas UniversityIT3 papers
- IRCCS Humanitas Research HospitalIT3 papers
- Leiden UniversityNL3 papers
- University of PisaIT3 papers
- Allen Institute for Cell ScienceUS2 papers
- Broad InstituteUS2 papers
9 papers · 1 filter
Integrating state-sequence analysis to uncover dynamic drug-utilization patterns to profile heart failure patients
Nicole Fontana, Laura Savaré, Francesca Ieva
Globally, the incidence of heart failure is increasing, and its principal treatment involves drug therapy. However, widespread non-adherence to therapies is prevalent among heart f…
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
Alberto Archetti, Francesca Ieva, Matteo Matteucci
Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are oft…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Causal effect of chemotherapy received dose intensity on survival outcome: a retrospective study in osteosarcoma
Marta Spreafico, Francesca Ieva, Marta Fiocco
This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. In…
Ensemble Methods for Multi-Organ Segmentation in CT Series
Leonardo Crespi, Paolo Roncaglioni, Damiano Dei +5
In the medical images field, semantic segmentation is one of the most important, yet difficult and time-consuming tasks to be performed by physicians. Thanks to the recent advancem…
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Leonardo Crespi, Mattia Portanti, Daniele Loiacono
Organ at Risk (OAR) segmentation from CT scans is a key component of the radiotherapy treatment workflow. In recent years, deep learning techniques have shown remarkable potential…
bAIoimage analysis: elevating the rate of scientific discovery -- as a community
Damian Edward Dalle Nogare, Matthew Hartley, Joran Deschamps +2
The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue…
Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach
Alessandra Ragni, Chiara Masci, Francesca Ieva +1
We introduce a novel statistical significance-based approach for clustering hierarchical data using semi-parametric linear mixed-effects models designed for responses with laws in…
Community-developed checklists for publishing images and image analysis
Christopher Schmied, Michael Nelson, Sergiy Avilov +51
Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. H…