Deeponcology.ai

DeepOncologys express goal is to act as facilitator between imaging science and clinical cancer research; integrating Oncology, artificial intelligence and computer vision, in clinically-significant imaging applications. We now use DL frequently in our work: proven to offer extraordinary prospects for precise tumour characterisation. At DeepOncology AI, we have devoted significant investment in achieving our goals, spending extensive time and financial resources on developing scientific expertise as deep-learning experts. Our interest in combining DL and Oncology ultimately leads to research into Liver cancer (one of the deadliest cancer types). While more than 600,000 people die each year as a result of Liver cancer, the number of tumours identified worldwide increases at an alarming rate. Early diagnosis and treatment is now more likely and possibly the most suitable means for cancer death reduction. In medical imaging, Computed Tomography (CT) images are widespread in detection and diagnosis of liver lesions. However, this involves manual image screening. This laborious task involves radiologists manually searching 3D CT scans. Therefore, there is a very real need for automated analysis tools, providing assistance to clinicians screening for liver metastases in CT images. Automated computerised detection tools may provide support to the radiologist, by improving feature detection. Automatic segmentation of hepatic liver lesions represents a vital step towards provision of accurate information for Oncologists. DeepOncology AI draws on several key approaches to automatic liver segmentation, and tumour identification in CT images, using an ensemble of convolutional neural networks (CNNs). It is this that provides rapid, accurate segmentation of multiple lesions.