Speaker: Ophir Yaniv

M.Sc. student under the supervision of Prof. Nahum Kiryati and Dr. Arnaldo Mayer

Sunday, January 26^th, 2020 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

Towards Automatic Lesion Detection in 3D Prostate MRI Scans

Abstract

Prostate cancer is the second most common cancer among men. Early detection is critical to the success of its treatment. Thanks to the excellent contrast it provides in soft tissues, prostate MRI has become the tool of choice in prostate cancer imaging.

We present a novel approach for the automated detection of prostate lesions in 3D MRI scans. Our methodology divides the workflow into two stages: 1) Segmentation of the prostate within the MRI scan to remove unrelated tissues. 2) Detection of suspected malignancies within the segmented prostate.

Previous methods of 3D prostate segmentation required high computational power and memory which are usually not available on the PCs used by radiologists. We propose a novel 3D deep neural network architecture, called V-net Light (VnL), that is based on a computationally efficient 3D Module, called 3D Light. The resulting network minimizes the number of parameters while maintaining state-of-the-art segmentation results.

Qualitative and quantitative validation of the proposed architecture will be presented.

To conclude, several directions for the lesion detection stage, to be developed in continuation of this research, will be discussed.

 (The talk will be given in English)

Speaker:     Dr. Oren Barkan
                    Microsoft, Israel

SUNDAY, January 12^th, 2020
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

VBNets: Learning Entity Representations via Variational Bayesian Networks

Abstract

Learning entity representations is an active research field. In the last decade, both the NLP and recommender systems communities introduced a plethora of methods for mapping words, items and users to vectors in a latent space. The vast majority of these works utilize implicit co-occurrences relations (e.g.  co-occurrences of words in text, co-consumption of items by users) for learning the latent entity vectors. Yet, often, additional side information in the form of explicit (e.g. hierarchical, semantic, syntactic) relations can be leveraged for learning finer embeddings. 

In this talk, we present Variational Bayesian Networks (VBNets) - A novel scalable hierarchical Bayesian model that utilizes both implicit and explicit relations for learning entity representations. VBNets are designed for Microsoft Store and Xbox services that handle around a billion users worldwide. Different from point estimate solutions that map entities to vectors and are usually over confident, VBNets map entities to densities in the latent space and hence model uncertainty. VBNets are based on analytical approximations of the intractable entities' posterior and the posterior predictive distribution of the data. We demonstrate the effectiveness of VBNets on linguistic, recommendations, and medical informatics tasks, where it is shown to outperform other alternative methods that facilitate Bayesian modeling with or without semantic priors. In addition, we show that VBNets produce superior representations for rare words and cold items. If time permits, we will give a brief overview of several recent deep learning works in the domains of deep neural attention mechanisms, multiview representation learning and inverse problems with applications for natural language understanding, recommender systems, computer vision, sound synthesis and biometrics. 

Short Bio
Oren Barkan is a Principal Researcher at Microsoft, where he was previously a post-doctoral researcher, collaborating with Microsoft Research UK and Microsoft Israel. Prior to that, he was with Google Research and IBM Research. He received his Ph.D. from Tel Aviv University, under the supervision of Prof. Amir Averbuch. His research interests are deep neural attention mechanisms, representation learning, multiview learning, Bayesian inference and inverse problems with applications for computer vision, natural language understanding, recommender systems, speech analysis, sound synthesis, biometrics, inflation forecasting, healthcare and medical informatics. He is the author of more than 40 research papers and patents.‏