Medical Imaging

This is the data as collected within the Study comparing different DVR techniques in terms of correctness, efficiency and perceived workload in the context of visceral surgery.

Retinal Fundus Multi-disease Image Dataset (RFMiD 2.0) is an auxiliary dataset to our previously published dataset. RFMiD 2.0 is a more challenging dataset to research society to develop the computer-based disease diagnosis system. Diabetic Retinopathy, cataracts, and refractive error in the eye are leading disease which causes permanent vision loss more frequently. Therefore, developing an AI-based model to classify these diseases is useful for ophthalmologists. This dataset consists of 860 images of frequently and rarely observed 51 diseases.

Due to the complex and unstructured nature of the intestine, 3D reconstruction and visual navigation are imperative for clinical endoscopists performing the skill-intensive colonoscopy. Unsupervised 3D reconstruction methods, as a mainstream paradigm in auto-driving scenarios, exploit warping loss to predict 6-DOF pose and depth information jointly. However, owing to illumination inconsistency, repeated texture regions, and non-Lambertian reflection, the geometry warping constraint cannot be efficiently applied to the colonic environment.

Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation.