Dendritic Consortium Multimodal Dataset

This dataset contains the training data for the CHIMERA - Combining HIstology, Medical imaging (radiology) and molEcular data for medical pRognosis and diAgnosis challenge. The CHIMERA Challenge aims to advance precision medicine in cancer care by addressing the critical need for multimodal data integration. Despite significant progress in AI, integrating transcriptomics, pathology, and radiology across clinical departments remains a complex challenge. Clinicians are faced with large, heterogeneous datasets that are difficult to analyze effectively. AI has the potential to unify multimodal data, but several technical barriers remain, such as defining appropriate fusion stages and handling missing modalities.

This dataset presents a multi-modal maritime dataset acquired in restricted waters in Pohang, South Korea. The sensor suite is composed of three LiDARs (one 64-channel LiDAR and two 32-channel LiDARs), a marine radar, two visual cameras used as a stereo camera, an infrared camera, an omnidirectional camera with 6 directions, an AHRS, and a GPS with RTK. The dataset includes the sensor calibration parameters and SLAM-based baseline trajectory. It was acquired while navigating a 7.5 km route that includes a narrow canal area, inner and outer port areas, and a near-coastal area. The aim of this dataset is to facilitate research on autonomous surface vehicles.

Rich multimodal embedding model that can vectorize interleaved text, content-rich images, and video. 32K context length.

Sample repository from a 2.53M+ image dataset covering Healthcare, STEM, Automotive, AutoCAD 3D, Infographic, PSD Design, and Complex Scene images for computer vision, image classification, object detection, visual understanding, and multimodal AI training.