Search Results for author:
Found 12 papers, 4 papers with code
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge
This report describes the submitted system to the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) challenge, which considers the ASR task with multi-speaker overlapping and Mandarin accent dynamics in the ICMC case.
Minimizing Sensor Allocation Cost for Crowdsensing On-street Parking Availability
This paper presents an integer programming-based optimal sensor allocation model to ensure the detection accuracy of the scheme while using the minimum number of sensing kits or probing vehicles.
RadTex: Learning Efficient Radiograph Representations from Text Reports
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
Image Classification with Consistent Supporting Evidence
We define consistent evidence to be both compatible and sufficient with respect to model predictions.
Multimodal Representation Learning via Maximization of Local Mutual Information
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
DEMI: Discriminative Estimator of Mutual Information
Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data.
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0. 99 for the semi-supervised model and 0. 87 for the pre-trained models.
Temporal Registration in Application to In-utero MRI Time Series
To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
Temporal Registration in In-Utero Volumetric MRI Time Series
We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series.
Crowdsourcing On-street Parking Space Detection
As the number of vehicles continues to grow, parking spaces are at a premium in city streets.
