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Found 12 papers, 8 papers with code
Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition
Ablation studies further validate the contributions of each module, highlighting the significance of advanced feature extraction and fusion strategies in enhancing emotion recognition performance.
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice.
An Integrated Artificial Intelligence Operating System for Advanced Low-Altitude Aviation Applications
This paper introduces a high-performance artificial intelligence operating system tailored for low-altitude aviation, designed to address key challenges such as real-time task execution, computational efficiency, and seamless modular collaboration.
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens.
Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge
Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment.
YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention
Accurate prediction of drug molecule solubility is crucial for therapeutic effectiveness and safety.
Calibration of the rating transition model for high and low default portfolios
In this paper we develop Maximum likelihood (ML) based algorithms to calibrate the model parameters in credit rating transition models.
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.
Adaptive Scheduling for Edge-Assisted DNN Serving
Based on this observation, we first design a novel scheduling algorithm to exploit the batching benefits of all requests that run the same DNN.
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
