Centre Antoine Lacassagne

Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma

Z. Khene, Pierre Bigot, N. Doumerc et al.

2022 · 48 Zit.

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications

Francesco Cremonesi, Marc Vesin, Sergen Cansiz et al.

2025 · 4 Zit.

Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma

Shamimeh Ahrari, Timothée Zaragori, Adeline Zinsz et al.

2025 · 2 Zit.

Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs

Bruno Fionda, Elisa Placidi, Mischa de Ridder et al.

2024 · 2 Zit.

Letter to the editor: “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

Antoine Iannessi, Hubert Beaumont, Anne Sophie Bertrand

2021 · 2 Zit.

The ins and outs of errors in oncology imaging: the DAC framework for radiologists

Antoine Iannessi, Hubert Beaumont, Carlos Aguillera et al.

2024 · 1 Zit.

Can we improve cost effectiveness of oncology clinical trials workflow? A prospective RECIST 1.1 study

Hubert Beaumont, Antoine Iannessi, Catherine Klifa et al.

2018 · 0 Zit.

200P Leveraging large language models for identifying CyberKnife radiotherapy in oligometastatic breast cancer: A time-saving tool for retrospective studies

R. Schiappa, S. Ben Dhia, Sara Contu et al.

2025 · 0 Zit.

Intelligence artificielle en oncologie radiothérapie : réinventer la formation et préserver l’esprit critique

L. Lahmi, Jean‐Emmanuel Bibault, Yannis Constantinidès et al.

2025 · 0 Zit.

P32 - AUTORECIST : utilisation de l'intelligence artificielle pour prédire la réponse RECIST

S. Contu, R. Schiappa, T. Villard et al.

2024 · 0 Zit.

CO10.2 - Comparaison de différentes méthodes de machine learning supervisé pour l'aide au diagnostic médical des nodules thyroïdiens

J. Gal, S. Benidir, G. D'andréa et al.

2023 · 0 Zit.

P55 - Automating patient pre-screening for oncology trials: a Python-based approach to optimize recruitment for the MOIO study in multiple sites

Renaud Schiappa, Lou-Anne Guillotel, G Danton et al.

2025 · 0 Zit.