Based on cutting-edge computer vision technology, our algorithm can swiftly detect and localize various lesions and organs in multi-modality medical images, such as detecting spine discs and vertebrae in CT or MRI, lung nodules in CT, cells in pathology images. It provides doctors with valuable references for diagnosis and therefore improve efficiency.
By integrating international common practice of diagnosis guidelines and the experience of top medical professionals, our algorithm can precisely classify lesions, which plays a key role in clinical diagnoses and helps minimize misdiagnosis of similar lesions.
Based on the analysis of medical images and clinical factors, our algorithm can diagnose the malignancy and severity of various diseases, such as the grading of anterior cruciate ligament tear, the discrimination of benign and malignant lung nodules, and to meet the need of mass screening and hierarchical diagnosis.
Our algorithm performs training based on small data sets to achieve pixel-level precise segmentation of multiple lesions and organs; conducts key parameter measurement and automatic, quantified analysis, such as radiotherapy target area delineation and pelvic tumor segmentation. This does not only release doctors from time-consuming, labor-intensive manual illustration work, but also fulfil the requirements of quantified diagnosis and personalized surgery planning scenarios.
With the registration of multi-modality data such as CT, MRI, and PET of the same body part or organ, our algorithm can achieve accurate fusion of different modalities and different sequence data, enabling more accurate qualitative grading and quantitative analysis of lesions.