Focus Group on Artificial Intelligence for Health

no code implementations • 13 Sep 2018 • Marcel Salathé, Thomas Wiegand, Markus Wenzel

Artificial Intelligence (AI) - the phenomenon of machines being able to solve problems that require human intelligence - has in the past decade seen an enormous rise of interest due to significant advances in effectiveness and use.

Decision Making

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

no code implementations • 22 Apr 2020 • Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, Thomas Wiegand

Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic.

BIG-bench Machine Learning

Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks

1 code implementation • 16 Apr 2021 • Johanna Vielhaben, Markus Wenzel, Eva Weicken, Nils Strodthoff

Predicting the binding of viral peptides to the major histocompatibility complex with machine learning can potentially extend the computational immunology toolkit for vaccine development, and serve as a key component in the fight against a pandemic.

Infant Brain Age Classification: 2D CNN Outperforms 3D CNN in Small Dataset

1 code implementation • 27 Dec 2021 • Mahdieh Shabanian, Markus Wenzel, John P. DeVincenzo

Determining if the brain is developing normally is a key component of pediatric neuroradiology and neurology.

Age Classification

From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology

1 code implementation • 11 Apr 2022 • Maximilian Springenberg, Annika Frommholz, Markus Wenzel, Eva Weicken, Jackie Ma, Nils Strodthoff

While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy.

Benchmarking Classification +2

Generative Adversarial Networks and Other Generative Models

no code implementations • 8 Jul 2022 • Markus Wenzel

Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images, and also generalizes to tasks in image analysis.

Anomaly Detection Image Generation +3

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

no code implementations • 11 Aug 2023 • Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.

Fairness

Insights Into the Inner Workings of Transformer Models for Protein Function Prediction

1 code implementation • 7 Sep 2023 • Markus Wenzel, Erik Grüner, Nils Strodthoff

Motivation: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent representations inside of transformer models, which were finetuned to Gene Ontology term and Enzyme Commission number prediction, can be inspected too.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1