Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resolution whole slide images (WSI). However, traditional pure vision models face challenges of redundant feature extraction, whereas existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy. To overcome these issues, we propose two innovative strategies: the mixed task-guided feature enhancement, which directs feature extraction toward lesion-related details across scales, and the prompt-guided detail feature completion, which integrates coarse- and fine-grained features from WSI based on specific prompts without compromising inference speed. Leveraging a comprehensive dataset of 490,000 samples from diverse pathology tasks-including cancer detection, grading, vascular and neural invasion identification, and so on-we trained the pathology-specialized LVLM, OmniPath. Extensive experiments demonstrate that this model significantly outperforms existing methods in diagnostic accuracy and efficiency, offering an interactive, clinically aligned approach for auxiliary diagnosis in a wide range of pathology applications.

Intel has moved to a new architecture called Kaby Lake for its PC chips, but it isn't done with the previous generation Skylake yet. The company will release new Xeon server chips based on Skylake in mid-2017, and they will boast big performance increases, said Barry Davis, general manager for the accelerated workload group at Intel. The Skylake Xeon chips will go into mainstream servers and could spark a big round of hardware upgrades, Davis said. Xeon chips aren't as visible as Intel's PC chips but remain extremely popular. Companies like Google, Facebook, and Amazon buy thousands of servers loaded with Xeon chips to power their search, social networking, and artificial intelligence tasks.