Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Autor: Ankolekar, Anshu, Boie, Sebastian, Abdollahyan, Maryam, Gadaleta, Emanuela, Hasheminasab, Seyed Alireza, Yang, Guang, Beauville, Charles, Dikaios, Nikolaos, Kastis, George Anthony, Bussmann, Michael, Khalid, Sara, Kruger, Hagen, Lambin, Philippe, Papanastasiou, Giorgos

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systemat

Externí odkaz: http://arxiv.org/abs/2408.05249

Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial

Autor: Zhong, Xian, Salahuddin, Zohaib, Chen, Yi, Woodruff, Henry C, Long, Haiyi, Peng, Jianyun, Udawatte, Nuwan, Casale, Roberto, Mokhtari, Ayoub, Zhang, Xiaoer, Huang, Jiayao, Wu, Qingyu, Tan, Li, Chen, Lili, Li, Dongming, Xie, Xiaoyan, Lin, Manxia, Lambin, Philippe

Artificial intelligence (AI)-based decision support systems have demonstrated value in predicting post-hepatectomy liver failure (PHLF) in hepatocellular carcinoma (HCC). However, they often lack transparency, and the impact of model explanations on

Externí odkaz: http://arxiv.org/abs/2408.03771

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Autor: Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, Starmans, Martijn P A

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinica

Externí odkaz: http://arxiv.org/abs/2309.12325

Land system governance shapes tick-related public and animal health risks

Autor: S.O Vanwambeke, E.F Lambin, P Meyfroidt, F.A Asaaga, C Millins, B.V Purse

Publikováno v: Journal of Land Use Science, Vol 19, Iss 1, Pp 78-96 (2024)

ABSTRACTLand cover and land use have established effects on hazard and exposure to vector-borne diseases. While our understanding of the proximate and distant causes and consequences of land use decisions has evolved, the focus on the proximate effec

Externí odkaz: https://doaj.org/article/c51f2f9f7e024cdaa5f75cac5118c57e

MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets

Autor: Kuang, Sheng, Woodruff, Henry C., Granzier, Renee, van Nijnatten, Thiemo J. A., Lobbes, Marc B. I., Smidt, Marjolein L., Lambin, Philippe, Mehrkanoon, Siamak

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological het

Externí odkaz: http://arxiv.org/abs/2301.02554