Saqib Qamar received his Ph.D. degree in Computer Science from Huazhong University of Science and Technology, China. His primary research interests are in the areas of Computer Vision, Medical Image Analysis, Image Segmentation, and Deep Learning. His research focuses on applying a deep learning approach to images to automatically detect and segmentation of different objects.
Saqib Qamar joined Magnus Andersson lab in Icelab as a postdoctoral researcher in 2022. He started to work on the segmentation of cells from 3D X-ray imaging. He also working on segmenting and classifying bacterial spore layers from Transmission Electron Microscopy (TEM) images. This work will be valuable for identifying and characterizing spore features in TEM images, reducing labor-intensive work as well as human bias. He likes exploring various domains because ML-based approaches could reduce manual dependency on tasks in domains.
Saqib Qamar, Rasmus Öberg, Dmitry Malyshev, Magnus Andersson "A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images". (2023) Accepted in Scientific Reports