M
Mehdi Moradi
Researcher at IBM
Publications - 140
Citations - 2700
Mehdi Moradi is an academic researcher from IBM. The author has contributed to research in topics: Deep learning & Feature (computer vision). The author has an hindex of 23, co-authored 136 publications receiving 1987 citations. Previous affiliations of Mehdi Moradi include BC Cancer Agency & Queen's University.
Papers
More filters
BookDOI
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Book ChapterDOI
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
TL;DR: A fast converging and computationally efficient network architecture for accurate segmentation and an exponential logarithmic loss which balances the labels not only by their relative sizes but also by their segmentation difficulties is proposed.
Proceedings ArticleDOI
Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation
TL;DR: For cardiac abnormality classification in chest X-rays, it is demonstrated that an order of magnitude less data is required with semi-supervised learning generative adversarial networks than with conventional supervised learning convolutional neural networks.
Journal ArticleDOI
High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models
James Monaco,John E. Tomaszewski,Michael Feldman,Ian S. Hagemann,Mehdi Moradi,Parvin Mousavi,Alexander Boag,Chris Davidson,Purang Abolmaesumi,Anant Madabhushi +9 more
TL;DR: A high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF).
Proceedings ArticleDOI
Chest x-ray generation and data augmentation for cardiovascular abnormality classification
TL;DR: This work investigates the use of GANs for producing chest X-ray images to augment a dataset and uses this dataset to train a convolutional neural network to classify images for cardiovascular abnormalities.