Welcome to the Deep Learning in Medical Imaging Lab
Research Focus Areas:
We focus on Machine Learning in Medical Image Analysis. In the past three years we have been focusing on Deep Learning and we bring these tools to the many important and challenging medical tasks: from image augmentation- to enable the physicians to visualize the image better and to detect earlier; to image segmentation, to separate out important areas within the image; to image quantification – to provide quantitative measurements, thus enabling more advanced quantitative analysis and tracking over time; to image labeling and classification – to support the radiologist and oncologist decision making process.
Projects in the Lab span many focus areas, as listed below. In each focus area we collaborate with clinical partners, who support us in the identification of the medical challenge, provide us with the data examples – and join us in the exploration of computational tools which we develop.
The work in the Lab is funded by both Israeli as well as International grants. Including: the Israeli Science Foundation (ISF
), as well as awards from INTEL
- MRI Brain Image Analysis (affiliated with the Sagol school of Brain research and Neuroscience): Augmenting resolution in MRI; Neurological diseases, Multiple-Sclerosis; CT lesion analysis;
- CT Abdomen analysis: In particular, Liver lesions – detection, segmentation, characterization in time (Collaborations with Radiology Dept, Stanford & Head of CT Abdomen Unit at Sheba medical center, Israel)
- Mammmography - microcalcification analysis
- Xray or Radiograph Analysis: In particular, Chest analysis. Identification of diseases with the lungs, such as enlarged heart, liquid, air and more.
- Uterine-cervix image analysis and indexing for cancer screening;
- Medical image retrieval from large archives (PACS)