Texture is a key feature in the visual diagnosis of medical settings. Manual inspection of specimens with a light microscope is still to date the gold standard. In collaboration with the Institute of Molecular Medicine in Finland (Adjunct Professor Johan Lundin), we explored high throughput computer assisted methods for automated analysis of digitized breast and colorectal cancer samples, and microbiological samples, e.g. malaria parasites.
More recently, in collaboration with Prof. Osmo Tervonen from Oulu University Hospital we investigated the problem of thorax disease diagnosis from x-ray images using an approach based on deep convolutional neural network.
Linder N, Konsti J, Turkki R, Rahtu E, Lundin M, Nordling S, Ahonen T, Pietikäinen M & Lundin J (2012)
Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagnostic Pathology 2012, 7:22.
Ojansivu V, Linder N, Rahtu E, Pietikäinen M, Lundin M, Joensuu H & Lundin J (2013) Automated classification of breast cancer morphology in histopathological images. Diagnostic Pathology 2013, 8 (Suppl. 1):S29.
Linder N, Turkki R, Walliander M, Mårtensson A, Diwan V, Rahtu E, Pietikäinen M, Lundin M & Lundin M (2014) A malaria diagnostic tool based on computer vision screening and visualization of plasmodium falciparum candidate areas in digitized blood smears. PLoS ONE, 9(8):e104855.
Chen J, Qi X, Tervonen O, Silven O, Zhao G & Pietikäinen M (2016) Thorax disease diagnosis using deep convolutional neural network. Proc. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'16).
Last updated: 26.8.2016