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Found 27 papers, 8 papers with code
Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations.
Dual-Bridging With Adversarial Noise Generation for Domain Adaptive rPPG Estimation
Under different cross-domain scenarios, the comprehensive results show the effectiveness of our method.
Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth
Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.
dm_control: Software and Tasks for Continuous Control
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.
MLOS: An Infrastructure for Automated Software Performance Engineering
In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions.
Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models
We use over 350, 000 Yelp reviews on 5, 000 restaurants to perform an ablation study on text preprocessing techniques.
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks
To make the network more robust to unanticipated noise perturbations, we use PGD to search for noise patterns that can trigger the network to give over-confident mistakes.
Event Outlier Detection in Continuous Time
In this work, we study and develop methods for detecting outliers in continuous-time event sequences, including unexpected absence and unexpected occurrences of events.
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
``Regressive point processes'' refer to point processes that directly model the dependency between an event and any past event, an example of which is a Hawkes process.
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO).
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
Multi-Sensor 3D Object Box Refinement for Autonomous Driving
We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving.
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care.
Emergent Coordination Through Competition
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics.
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations.
Hierarchical visuomotor control of humanoids
We aim to build complex humanoid agents that integrate perception, motor control, and memory.
Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
Ranked #1 on
Nuclear Segmentation
on Cell17
Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection
3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention.
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
The focal loss is further utilized for more effective end-to-end learning.
Detection of Abnormal Input-Output Associations
We study a novel outlier detection problem that aims to identify abnormal input-output associations in data, whose instances consist of multi-dimensional input (context) and output (responses) pairs.
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Finally, we show that using our PG method we can optimize any of the metrics, including the proposed SPIDEr metric which results in image captions that are strongly preferred by human raters compared to captions generated by the same model but trained to optimize MLE or the COCO metrics.
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map.
