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Found 17 papers, 6 papers with code
HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction.
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering.
IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models
Second, we fine-tune the image quality captioning VLM on the CT-IQA dataset to generate quality descriptions.
Federated learning-outcome prediction with multi-layer privacy protection
To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model.
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning
Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline.
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving.
Do LLMs Implicitly Exhibit User Discrimination in Recommendation? An Empirical Study
Recently, Large Language Models (LLMs) have enhanced user interaction, enabling seamless information retrieval and recommendations.
InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations.
Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs
Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potential in synthesizing novel views with a limited number of observed viewpoints.
Delving into Semantic Scale Imbalance
Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations.
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
We then establish a strong baseline that scores a R@1 of 46. 2% on Few-Shot and 76. 6% on Zero-Shot on our dataset.
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration
Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data.
An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net
Conclusions:Compared with the results of our previous studies and other studies, the method in this paper achieves a very competitive sensitivity with less training data and maintains a low false positive rate. As the only method currently using 3D U-Net for aneurysm detection, it proves the feasibility and superior performance of this network in aneurysm detection, and also explores the potential of the channel attention mechanism in this task.
Deep Learning-based Segmentation of Cerebral Aneurysms in 3D TOF-MRA using Coarse-to-Fine Framework
The segmentation metrics we used include DSC, HD, and VS.
Crystal structure prediction of materials with high symmetry using differential evolution
Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry.
AlphaCrystal: Contact map based crystal structure prediction using deep learning
To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations.
Protein Structure Prediction
Materials Science
MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials.
Materials Science Computational Physics
