Chenglu Zhu
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    • PathAsst: Generative Foundation AI Assistant for Pathology
    • DPA-P2PNet: Deformable Proposal-Aware P2PNet for Accurate Point-Based Cell Detection
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    • PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
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    • Test-Time Training for Semantic Segmentation with Output Contrastive Loss
    • Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
    • Automatic and accurate segmentation of peripherally inserted central catheter (PICC) from chest X-rays using multi-stage attention-guided learning
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    • Category Separation For Weakly Supervised Multi-Class Cell Counting
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    • Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning
    • End-to-end cell recognition by point annotation
    • Weakly Supervised Classification using Multi-Level Instance-Aware Optimization on Cervical Cytologic Image
    • Weakly supervised learning for cell recognition in immunohistochemical cytoplasm staining images
    • Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images
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scikit-learn

Oct 26, 2023 · 1 min read
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scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Oct 26, 2023
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← PyTorch Oct 26, 2023

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