Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation
JL Wichmann, MJ Willemink… - Investigative …, 2020 - journals.lww.com
Although artificial intelligence (AI) has been a focus of medical research for decades, in the
last decade, the field of radiology has seen tremendous innovation and also public focus …
last decade, the field of radiology has seen tremendous innovation and also public focus …
Learning everything about anything: Webly-supervised visual concept learning
SK Divvala, A Farhadi… - Proceedings of the IEEE …, 2014 - openaccess.thecvf.com
Recognition is graduating from labs to real-world applications. While it is encouraging to see
its potential being tapped, it brings forth a fundamental challenge to the vision researcher …
its potential being tapped, it brings forth a fundamental challenge to the vision researcher …
Image search—from thousands to billions in 20 years
This article presents a comprehensive review and analysis on image search in the past 20
years, emphasizing the challenges and opportunities brought by the astonishing increase of …
years, emphasizing the challenges and opportunities brought by the astonishing increase of …
Love thy neighbors: Image annotation by exploiting image metadata
Some images that are difficult to recognize on their own may become more clear in the
context of a neighborhood of related images with similar social-network metadata. We build …
context of a neighborhood of related images with similar social-network metadata. We build …
Contention-free execution of automotive applications on a clustered many-core platform
Next generations of compute-intensive real-time applications in automotive systems will
require more powerful computing platforms. One promising power-efficient solution for such …
require more powerful computing platforms. One promising power-efficient solution for such …
Multi-modal multi-scale deep learning for large-scale image annotation
Image annotation aims to annotate a given image with a variable number of class labels
corresponding to diverse visual concepts. In this paper, we address two main issues in large …
corresponding to diverse visual concepts. In this paper, we address two main issues in large …
Webly supervised semantic segmentation
B Jin, MV Ortiz Segovia… - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
We propose a weakly supervised semantic segmentation algorithm that uses image tags for
supervision. We apply the tags in queries to collect three sets of web images, which encode …
supervision. We apply the tags in queries to collect three sets of web images, which encode …
Accounttrade: Accountable protocols for big data trading against dishonest consumers
We propose AccountTrade, a set of accountable protocols, for big data trading among
dishonest consumers. To secure the big data trading environment, our protocols achieve …
dishonest consumers. To secure the big data trading environment, our protocols achieve …
Advances in deep learning approaches for image tagging
The advent of mobile devices and media cloud services has led to the unprecedented
growth of personal photo collections. One of the fundamental problems in managing the …
growth of personal photo collections. One of the fundamental problems in managing the …
Learning to detect salient object with multi-source weak supervision
High-cost pixel-level annotations makes it appealing to train saliency detection models with
weak supervision. However, a single weak supervision source hardly contain enough …
weak supervision. However, a single weak supervision source hardly contain enough …