8 research outputs found

    UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training

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    Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small scale, yielding the bottleneck of generalization. Towards a more effective and scalable paradigm, we propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics. Different from previous pre-training techniques for the unitary vision or textual feature, or with the brute-force alignment between vision and language information, we leverage the unique characteristic of report information in different granularity to build a hierarchical alignment mechanism, which strengthens the efficiency in feature learning. Our UniBrain is validated on three real world datasets with severe class imbalance and the public BraTS2019 dataset. It not only consistently outperforms all state-of-the-art diagnostic methods by a large margin and provides a superior grounding performance but also shows comparable performance compared to expert radiologists on certain disease types

    Study on Soil Displacement Fields around the Expanded Body of Drill-Expanded Concrete Piles Based on DIC Technique

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    The soil displacement field around a drill-expanded concrete pile is noticeably different from that of an equivalent section pile placed under axial load due to the mutual embedment between the expanded body and the soil. It is important to study the soil displacement field around drill-expanded concrete piles in order to understand the mechanisms of interaction between the pile and the soil. First, the model test of the half-face pile installed in undisturbed soil and the model test of the half-face pile installed in sand were used to study the soil displacement field around the pile. Then, the entire process of the soil displacement field’s formation and development under the load was observed by using digital image correlation (DIC) techniques. Finally, numerical simulation was used to verify the results of the model tests. The results show that the displacement characteristics of the soil around the pile in the undisturbed soil and sand are basically the same. There is a clear soil compression zone under the expanded body, and the magnitude and density of the displaced soil in the compression zone are much higher than in other areas. Both the vertical displacement and the horizontal displacement gradually decrease as the distance from the expanded body and the burial depth increase. The horizontal displacement of the soil under the expanded body follows a trend of first moving toward the pile body and then moving away from it. The results of the numerical simulation are basically consistent with the results of the model test, indicating that the results of the model test are relatively reliable

    Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data

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    MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online