Perspective on multi-modal modeling for biomarker discovery in oncology
Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalized medicine.
Deep learning for monkeypox
The Gevaert lab spearheaded by visiting scholar Dr. Alexander Thieme has developed a model that is able to distinguish mpox skin lesions from other skin lesions.
Imaging genomics: data fusion in uncovering disease heritability
Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable.
AI based analysis of imaging, clinical & lab data to triage COVID19 patients.
We took advantage of a large cohort of Chinese PCR-confirmed COVID19 patients to embark on a biomedical data fusion project to triage COVID19 patients.
Metalearning in oncology
RNA sequencing has emerged as a promising approach in cancer prognosis as RNA sequencing becomes more easily and affordable.