Rapid advancements in large multimodal models (LMMs) have significantly enhanced their applications in pathology, particularly in image classification, pathology image description, and whole slide image (WSI) classification. In pathology, WSIs represent gigapixel-scale images composed of thousands of image patches. Therefore, both patch-level and WSI-level evaluations are essential and inherently interconnected for assessing LMM capabilities. In this work, we propose PathBench, which comprises three subsets at both patch and WSI levels, to refine and enhance the validation of LMMs. At the patch-level, evaluations using existing multi-choice Q&A datasets reveal that some LMMs can predict answers without genuine image analysis. To address this, we introduce PatchVQA, a large-scale visual question answering (VQA) dataset containing 5,382 images and 6,335 multiple-choice questions designed with distractor options to prevent shortcut learning. These new questions are rigorously validated by professional pathologists to ensure reliable model assessments. At the WSI-level, current efforts primarily focus on image classification tasks and lack diverse validation datasets for multimodal models. To address this, we generate a detailed WSI report dataset through an innovative approach that integrates detailed patch descriptions generated by foundational models into comprehensive WSI reports. These are then combined with physician-written reports corresponding to TCGA WSIs, resulting in WSICap, a detailed report dataset containing 7,000 samples. Based on WSICap, we further develop a WSI-level VQA dataset, WSIVQA, to serve as a validation set for WSI LMMs. Using these PathBench subsets, we conduct extensive experiments to benchmark the performance of state-of-the-art LMMs at both the patch and WSI levels. The proposed dataset is available at https://github.com/superjamessyx/PathBench.

With the development of communications, networking, and information technology, Crowdsensed Data Trading (CDT) becomes a novel data trading paradigm. In CDT, the data requesters publish crowdsensing tasks with specific data requirements, and then workers complete these tasks, upload the data and obtain corresponding rewards. To efficiently deal with data trading, most of the existing CDT systems assume a trusted centralized platform. However, we argue that the platform may collude with workers or requesters to trick others for achieving more benefits. For example, according to the workers’ uploaded data, the platform can modify the reward functions by colluding with the requester. Similarly, the platform might collude with workers to let them know the reward function, then workers could forge data. Meanwhile, requesters and workers may also be malicious. For example, requesters may post tasks but fail to pay and workers can upload wrong data to mislead the system. To solve the above problems, we combine the Crowdsensed Data Trading system with intelligent Blockchain (CDT-B), which contains a smart contract called CDToken. As a credible third-party, the CDToken is used to record the requesters’ reward function and workers’ data uploading function to avoid targeted trick. At the same time, we not only design a Data Uploading and Preprocessing (DUP) mechanism in CDToken to collect and process the workers’ sensed data, but also propose a Grouping Truth Discovery (GTD) to evaluate their data quality for determining the payments. Moreover, to hold a large number of requesters and workers in CDT-B, we propose a Layered Sharding blockchain based on Membership Degree (LSMD) to solve the blockchain inefficiency problem. Finally, we deploy CDToken to an experimental environment based on Ethereum and demonstrate its efficient performance and practicability.