Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations.
no code implementations • 13 Aug 2020 • Eduardo Mortani Barbosa Jr., Warren B. Gefter, Rochelle Yang, Florin C. Ghesu, Si-Qi Liu, Boris Mailhe, Awais Mansoor, Sasa Grbic, Sebastian Piat, Guillaume Chabin, Vishwanath R S., Abishek Balachandran, Sebastian Vogt, Valentin Ziebandt, Steffen Kappler, Dorin Comaniciu
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.
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.
In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.
no code implementations • 5 May 2020 • Si-Qi Liu, Bogdan Georgescu, Zhoubing Xu, Youngjin Yoo, Guillaume Chabin, Shikha Chaganti, Sasa Grbic, Sebastian Piat, Brian Teixeira, Abishek Balachandran, Vishwanath RS, Thomas Re, Dorin Comaniciu
Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions.
no code implementations • 2 Apr 2020 • Shikha Chaganti, Abishek Balachandran, Guillaume Chabin, Stuart Cohen, Thomas Flohr, Bogdan Georgescu, Philippe Grenier, Sasa Grbic, Si-Qi Liu, François Mellot, Nicolas Murray, Savvas Nicolaou, William Parker, Thomas Re, Pina Sanelli, Alexander W. Sauter, Zhoubing Xu, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu
Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
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.
In this work, we study and develop methods for detecting outliers in continuous-time event sequences, including unexpected absence and unexpected occurrences of events.
``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.
1 code implementation • • Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Si-Qi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO).
1 code implementation • • H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Si-Qi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin Riedmiller, Matthew M. Botvinick
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.
We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving.
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.
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics.
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.
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations.
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
Ranked #1 on Nuclear Segmentation on Cell17
3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention.
The focal loss is further utilized for more effective end-to-end learning.
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.
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.
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.