20 citations · 22 across the 2 of their papers we have counts for
8 papers · 1 filter
Sequential Ensembling for Semantic Segmentation
Rawal Khirodkar, Brandon Smith, Siddhartha Chandra +2
Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications. In…
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particu…
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have…
Autofocus Layer for Semantic Segmentation
Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha +4
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus lay…
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh +4
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of me…
Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
Yoad Lewenberg, Yoram Bachrach, Sukrit Shankar +1
We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as…