Search Results for author:
Found 11 papers, 10 papers with code
Baichuan-Omni-1.5 Technical Report
We introduce Baichuan-Omni-1. 5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities.
ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models
This process often delays AI benefits, as human-centric data creation and AI-centric model development are treated as separate, sequential steps.
Analyzing Tumors by Synthesis
We also present case studies in the liver, pancreas, and kidneys reveal that AI trained on synthetic tumors can achieve performance comparable to, or better than, AI only trained on real data.
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking
AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms.
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme.
From Pixel to Cancer: Cellular Automata in Computed Tomography
We apply these three generic rules to simulate tumor development--from pixel to cancer--using cellular automata.
Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?
Human experts revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from these revised annotations.
Towards Generalizable Tumor Synthesis
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
