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Michal Drozdzal

Alumni

Publications

Learnable Explicit Density for Continuous Latent Space and Variational Inference
Chin-wei Huang
Ahmed Touati
Laurent Dinh
Michal Drozdzal
Mohammad Havaei
Laurent Charlin
Aaron Courville
In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its correspon… (voir plus)ding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any complicated posterior. Our analysis results in a unified approach to parameterizing a VAE, without the need to restrict ourselves to use factorial Gaussians in the latent real space.
2017-10-05
arXiv (prépublication)
doi.org
arxiv.org
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
David Vázquez
Jorge Bernal
F. Javier Sánchez
Gloria Fernández-Esparrach
Antonio M. López
Adriana Romero
Michal Drozdzal
Aaron Courville
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to… (voir plus) perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.
2017-07-25
Journal of Healthcare Engineering (publié)
doi.org
arxiv.org
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Simon Jégou
Michal Drozdzal
David Vázquez
Adriana Romero
Yoshua Bengio
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation arch… (voir plus)itecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions. Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. Code to reproduce the experiments is available here : https://github.com/SimJeg/FC-DenseNet/blob/master/train.py
2016-11-27
arXiv (prépublication)
doi.org
arxiv.org