Search Results for author: Sasa Grbic

Found 23 papers, 2 papers with code

Self-supervised Learning from 100 Million Medical Images

no code implementations4 Jan 2022 Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Dominik Neumann, Pragneshkumar Patel, R. S. Vishwanath, James M. Balter, Yue Cao, Sasa Grbic, Dorin Comaniciu

Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples.

Computed Tomography (CT) Contrastive Learning +1

You May Need both Good-GAN and Bad-GAN for Anomaly Detection

no code implementations29 Sep 2021 Riqiang Gao, Zhoubing Xu, Guillaume Chabin, Awais Mansoor, Florin-Cristian Ghesu, Bogdan Georgescu, Bennett A. Landman, Sasa Grbic

A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.

Anatomy Anomaly Detection

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

no code implementations13 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.

Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation

no code implementations5 Aug 2020 Sebastian Guendel, Arnaud Arindra Adiyoso Setio, Sasa Grbic, Andreas Maier, Dorin Comaniciu

However, because of the limited availability of scans containing nodules and the subtle properties of nodules in CXRs, state-of-the-art methods do not perform well on nodule classification.

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

no code implementations8 Jul 2020 Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R. S. Vishwanath, Abishek Balachandran, James M. Balter, Yue Cao, Ramandeep Singh, Subba R. Digumarthy, Mannudeep K. Kalra, Sasa Grbic, Dorin Comaniciu

In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e. g., by 8% to 0. 91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.

Anatomy Classification +1

Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images

no code implementations18 Mar 2020 Donghao Zhang, Si-Qi Liu, Shikha Chaganti, Eli Gibson, Zhoubing Xu, Sasa Grbic, Weidong Cai, Dorin Comaniciu

In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.

3D Reconstruction Decision Making +2

No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks

no code implementations8 Mar 2020 Si-Qi Liu, Arnaud Arindra Adiyoso Setio, Florin C. Ghesu, Eli Gibson, Sasa Grbic, Bogdan Georgescu, Dorin Comaniciu

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.

Adversarial Attack Lung Nodule Detection

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment

no code implementations18 Jun 2019 Florin C. Ghesu, Bogdan Georgescu, Eli Gibson, Sebastian Guendel, Mannudeep K. Kalra, Ramandeep Singh, Subba R. Digumarthy, Sasa Grbic, Dorin Comaniciu

We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data.

Classification General Classification

Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels

no code implementations15 May 2019 Sebastian Guendel, Florin C. Ghesu, Sasa Grbic, Eli Gibson, Bogdan Georgescu, Andreas Maier, Dorin Comaniciu

Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities.

Classification General Classification +1

Class-Aware Adversarial Lung Nodule Synthesis in CT Images

no code implementations28 Dec 2018 Jie Yang, Si-Qi Liu, Sasa Grbic, Arnaud Arindra Adiyoso Setio, Zhoubing Xu, Eli Gibson, Guillaume Chabin, Bogdan Georgescu, Andrew F. Laine, Dorin Comaniciu

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.

Binary Classification General Classification

Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images

no code implementations25 May 2018 Zhoubing Xu, Yuankai Huo, Jin-Hyeong Park, Bennett Landman, Andy Milkowski, Sasa Grbic, Shaohua Zhou

However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e. g., low contrast and large variations, but also the heterogeneity across tasks, i. e., one classification task for all views, and then one landmark detection task for each relevant view.

Classification General Classification +1

Select, Attend, and Transfer: Light, Learnable Skip Connections

no code implementations14 Apr 2018 Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.

Decoder Segmentation

Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

no code implementations12 Mar 2018 Sebastian Guendel, Sasa Grbic, Bogdan Georgescu, Kevin Zhou, Ludwig Ritschl, Andreas Meier, Dorin Comaniciu

To foster future research we demonstrate the limitations of the current benchmarking setup and provide new reference patient-wise splits for the used data sets.

Benchmarking

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

no code implementations25 Jul 2017 Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, Dorin Comaniciu

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.

Decoder Liver Segmentation +1

An Artificial Agent for Robust Image Registration

1 code implementation30 Nov 2016 Rui Liao, Shun Miao, Pierre de Tournemire, Sasa Grbic, Ali Kamen, Tommaso Mansi, Dorin Comaniciu

The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).

Image Registration Medical Image Registration

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