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Found 12 papers, 6 papers with code
生成模型在层次结构极限多标签文本分类中的应用(Generation Model for Hierarchical Extreme Multi-label Text Classification)
“层次结构极限多标签文本分类是自然语言处理研究领域中一个重要而又具有挑战性的课题。该任务类别标签数量巨大且自成体系, 标签与标签之间还具有不同层级间的依赖关系或同层次间的相关性, 这些特性进一步增加了任务难度。该文提出将层次结构极限多标签文本分类任务视为序列转换问题, 将输出标签视为序列, 从而可以直接从数十万标签中生成与文本相关的类别标签。通过软约束机制和词表复合映射在解码过程中利用标签之间的层次结构与相关信息。实验结果表明, 该文提出的方法与基线模型相比取得了有意义的性能提升。进一步分析表明, 该方法不仅可以捕获利用不同层级标签之间的上下位关系, 还对极限多标签体系自身携带的噪声具有一定容错能力。”
Multi Label Text Classification
Multi-Label Text Classification
+1
Circle Representation for Medical Instance Object Segmentation
Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e. g., cells, glomeruli, and nuclei).
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization
We show that even with just one extra line of code, the overfitting issues in underperforming architectures can be alleviated such that their performance gaps with the high-performing counterparts can be largely closed despite their distinct configurations, mitigating the need for architecture tuning.
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation
In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos.
Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis
Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease, characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation.
Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology
Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health.
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior
Existing methods mostly handcraft or search for the architecture from a large design space, due to the lack of understanding on how the architectural choice corresponds to the image.
Learning Reconstructability for Drone Aerial Path Planning
To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning.
Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting
Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e. g., T1 and T2 relaxation times).
Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset
We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction.
VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping
The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world.
Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network
However, most of the previous deep learning work does not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the amygdala and its subregions.
