Jakob Nikolas Kather

2 months ago | biorxiv.org | Verena Bitto |Xiaofeng Jiang |Michael Baumann |Jakob Nikolas Kather

AbstractComputational pathology-based models are becoming increasingly popular for extracting biomarkers from images of cancer tissue. However, their validity is often only demonstrated on a single unseen validation cohort, limiting insights into their generalizability and posing challenges for explainability. In this study, we developed models to predict overall survival using haematoxylin and eosin (H&E) slides from FFPE samples in head and neck squamous cell carcinoma (HNSCC).

2 months ago | fis.tu-dresden.de | Magdalena Wekenborg |Stephen Gilbert |Jakob Nikolas Kather

Artificial Intelligence (AI) is revolutionizing healthcare, but its true impact depends on seamless human interaction. While most research focuses on technical metrics, we lack frameworks to measure the compatibility or synergy of real-world human-AI interactions in healthcare settings. We propose a multimodal toolkit combining ecological momentary assessment, quantitative observations, and baseline measurements to optimize AI implementation.

Dec 5, 2024 | fis.tu-dresden.de | Charlotte Syrykh |Jakob Nikolas Kather |Camille Laurent |Université de Toulouse

The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures.

Oct 22, 2024 | onlinelibrary.wiley.com | Charlotte Syrykh |Jakob Nikolas Kather |TUD Dresden |Camille Laurent

AI artificial intelligence AUCROC area under the receiver operating characteristic curve CLL chronic lymphocytic leukemia/lymphocytic lymphoma COO Cell Of Origin DLBCL diffuse large B-cell lymphoma FISH fluorescence in situ hybridization FL follicular lymphoma H&E haematoxylin-eosin IPI international prognostic index MIPI-b biologic-Mantle cell lymphoma international prognostic index ML machine learning NLP natural language processing WSI whole slide image Introduction Lymphomas are among the...

Sep 5, 2024 | nature.com | Jakob Nikolas Kather

The development of clinically relevant artificial intelligence (AI) models has traditionally required access to extensive labelled datasets, which inevitably centre AI advances around large centres and private corporations. Data availability has also dictated the development of AI applications: most studies focus on common cancer types, and leave rare diseases behind.