Publications (14)
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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. Despite their popularity, most a…
The Future of Digital Health with Federated Learning
Nicola Rieke, Jonny Hancox, Wenqi Li +14
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…
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
Wadim Kehl, Fausto Milletari, Federico Tombari +2
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder…
TOMAAT: volumetric medical image analysis as a cloud service
Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi
Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art,…
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
Dong Yang, Holger Roth, Ziyue Xu +3
Deep neural network (DNN) based approaches have been widely investigated and deployed in medical image analysis. For example, fully convolutional neural networks (FCN) achieve the…
Interactive segmentation of medical images through fully convolutional neural networks
Tomas Sakinis, Fausto Milletari, Holger Roth +7
Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. M…
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
Gerda Bortsova, Michael Sterr, Lichao Wang +6
Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understo…
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll +8
In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough vo…
NeurReg: Neural Registration and Its Application to Image Segmentation
Wentao Zhu, Andriy Myronenko, Ziyue Xu +5
Registration is a fundamental task in medical image analysis which can be applied to several tasks including image segmentation, intra-operative tracking, multi-modal image alignme…
Adapting Self-Supervised Learning for Computational Pathology
Eric Zimmermann, Neil Tenenholtz, James Hall +8
Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision. This property makes…
Weakly supervised segmentation from extreme points
Holger Roth, Ling Zhang, Dong Yang +4
Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically r…
Straight to the point: reinforcement learning for user guidance in ultrasound
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 r…
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Ziyue Xu, Xiaosong Wang, Hoo-Chang Shin +5
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease. Conventionally, s…
CFCM: Segmentation via Coarse to Fine Context Memory
Fausto Milletari, Nicola Rieke, Maximilian Baust +2
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yield…