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Found 13 papers, 3 papers with code
ShorterBetter: Guiding Reasoning Models to Find Optimal Inference Length for Efficient Reasoning
Reasoning models such as OpenAI o3 and DeepSeek-R1 have demonstrated strong performance on reasoning-intensive tasks through extended Chain-of-Thought (CoT) prompting.
Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRS
were analyzed for reliability of within- and between- sessions using the coefficient of variation (CV) and intraclass correlation coefficient (ICC), and for reproducibility of across the machines using correlation coefficient.
EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making.
Enhancing Transformers for Generalizable First-Order Logical Entailment
This paper investigates the generalizable first-order logical reasoning ability of transformers with their parameterized knowledge and explores ways to improve it.
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues.
Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization
However, due to the differences between multimodal images and intraoperative image deformation caused by tissue displacement and removal during surgery, effective registration of preoperative and intraoperative multimodal images faces significant challenges.
Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024
In this paper, we summarize the methods and experimental results we proposed for Task 2 in the learn2reg 2024 Challenge.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal
Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends
In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently.
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization
We meticulously analyze the corpus using well-known metrics, focusing on the style of the summaries and the complexity of the summarization task.
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
