2k citations · 7k across the 39 of their papers we have counts for
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Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Avi Ben-Cohen, Roey Mechrez, Noa Yedidia +1
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in imag…
Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training
Ophir Gozes, Hayit Greenspan
The abundance of overlapping anatomical structures appearing in chest radiographs can reduce the performance of lung pathology detection by automated algorithms (CAD) as well as th…
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Maayan Frid-Adar, Avi Ben-Cohen, Rula Amer +1
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutiona…
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Liyan Sun, Jiexiang Wang, Yue Huang +3
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervise…
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
Yaniv Shachor, Hayit Greenspan, Jacob Goldberger
In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, w…
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using lar…
Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection
Avi Ben-Cohen, Eyal Klang, Stephen P. Raskin +5
In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial…
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
Maayan Frid-Adar, Eyal Klang, Michal Amitai +2
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first u…
Anatomical Data Augmentation For CNN based Pixel-wise Classification
Avi Ben-Cohen, Eyal Klang, Michal Marianne Amitai +2
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as anot…