Search Results for author: Fausto Milletari

Found 14 papers, 3 papers with code

Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation

no code implementations10 Jun 2020 Dong Yang, Holger Roth, Ziyue Xu, Fausto Milletari, Ling Zhang, Daguang Xu

For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation.

Data Augmentation Medical Image Segmentation +2

The Future of Digital Health with Federated Learning

no code implementations18 Mar 2020 Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.

Federated Learning

Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network

no code implementations MIDL 2019 Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu

In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.

Image Generation

NeurReg: Neural Registration and Its Application to Image Segmentation

1 code implementation4 Oct 2019 Wentao Zhu, Andriy Myronenko, Ziyue Xu, Wenqi Li, Holger Roth, Yufang Huang, Fausto Milletari, Daguang Xu

Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.

Image Registration Multi-Task Learning +1

Weakly supervised segmentation from extreme points

no code implementations2 Oct 2019 Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu

Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.

Weakly supervised segmentation

Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network

no code implementations8 Jul 2019 Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu

Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.

Image Generation

Interactive segmentation of medical images through fully convolutional neural networks

no code implementations19 Mar 2019 Tomas Sakinis, Fausto Milletari, Holger Roth, Panagiotis Korfiatis, Petro Kostandy, Kenneth Philbrick, Zeynettin Akkus, Ziyue Xu, Daguang Xu, Bradley J. Erickson

Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction.

Computed Tomography (CT) Interactive Segmentation +1

Straight to the point: reinforcement learning for user guidance in ultrasound

no code implementations2 Mar 2019 Fausto Milletari, Vighnesh Birodkar, Michal Sofka

Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders.

reinforcement-learning

CFCM: Segmentation via Coarse to Fine Context Memory

1 code implementation4 Jun 2018 Fausto Milletari, Nicola Rieke, Maximilian Baust, Marco Esposito, Nassir Navab

Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.

Semantic Segmentation

TOMAAT: volumetric medical image analysis as a cloud service

no code implementations19 Mar 2018 Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi

Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems.

Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields

no code implementations26 Aug 2016 Gerda Bortsova, Michael Sterr, Lichao Wang, Fausto Milletari, Nassir Navab, Anika Böttcher, Heiko Lickert, Fabian Theis, Tingying Peng

A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.

Mitosis Detection

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

21 code implementations15 Jun 2016 Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.

Semantic Segmentation Volumetric Medical Image Segmentation

Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound

no code implementations26 Jan 2016 Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll, Annika Plate, Verena Rozanski, Juliana Maiostre, Johannes Levin, Olaf Dietrich, Birgit Ertl-Wagner, Kai Bötzel, Nassir Navab

In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs).

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