Machine learning and radiology
In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …
radiology. We focused on six categories of applications in radiology: medical image …
A survey on deep learning-based architectures for semantic segmentation on 2d images
Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary
ability of convolutional neural networks (CNN) in creating semantic, high-level and …
ability of convolutional neural networks (CNN) in creating semantic, high-level and …
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
We present a novel and practical deep fully convolutional neural network architecture for
semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine …
semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine …
Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding
We present a deep learning framework for probabilistic pixel-wise semantic segmentation,
which we term Bayesian SegNet. Semantic segmentation is an important tool for visual …
which we term Bayesian SegNet. Semantic segmentation is an important tool for visual …
Simultaneous detection and segmentation
We aim to detect all instances of a category in an image and, for each instance, mark the
pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS) …
pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS) …
Revisiting point cloud shape classification with a simple and effective baseline
Processing point cloud data is an important component of many real-world systems. As
such, a wide variety of point-based approaches have been proposed, reporting steady …
such, a wide variety of point-based approaches have been proposed, reporting steady …
Deep filter banks for texture recognition and segmentation
Research in texture recognition often concentrates on the problem of material recognition in
uncluttered conditions, an assumption rarely met by applications. In this work we conduct a …
uncluttered conditions, an assumption rarely met by applications. In this work we conduct a …
Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling
We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling.
SegNet has several attractive properties;(i) it only requires forward evaluation of a fully learnt …
SegNet has several attractive properties;(i) it only requires forward evaluation of a fully learnt …
How to generate cryptographically strong sequences of pseudo random bits
M Blum, S Micali - Providing Sound Foundations for Cryptography: On …, 2019 - dl.acm.org
We introduce a new method of generating sequences of Pseudo Random Bits. Any such
method implies, directly or indirectly, a definition of Randomness. Much effort has been …
method implies, directly or indirectly, a definition of Randomness. Much effort has been …
[PDF][PDF] What is a good evaluation measure for semantic segmentation?.
In this work, we consider the evaluation of the semantic segmentation task. We discuss the
strengths and limitations of the few existing measures, and propose new ways to evaluate …
strengths and limitations of the few existing measures, and propose new ways to evaluate …