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Found 12 papers, 9 papers with code
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos
We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos.
Frequency-Modulated Point Cloud Rendering with Easy Editing
We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing.
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images.
Learning Shape Primitives via Implicit Convexity Regularization
In this work, a novel regularization term named Implicit Convexity Regularization (ICR) imposed on implicit primitive learning is proposed to tackle this problem.
Boosting Point Clouds Rendering via Radiance Mapping
In this work, we focus on boosting the image quality of point clouds rendering with a compact model design.
Representation-Agnostic Shape Fields
In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning.
Learning Black-Box Attackers with Transferable Priors and Query Feedback
This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available.
AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness.
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules.
Reinventing 2D Convolutions for 3D Images
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.
Evaluating and Boosting Uncertainty Quantification in Classification
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making.
