no code implementations • 14 Jan 2020 • Devinder Kumar, Parthipan Siva, Paul Marchwica, Alexander Wong
As such, there has been a recent focus on unsupervised learning approaches to mitigate the data annotation issue; however, current approaches in literature have limited performance compared to supervised learning approaches as well as limited applicability for adoption in new environments.
no code implementations • 28 Jul 2019 • Devinder Kumar, Parthipan Siva, Paul Marchwica, Alexander Wong
There has been recent interest in tackling this challenge using cross-domain approaches, which leverages data from source domains that are different than the target domain.
no code implementations • 21 Apr 2019 • Devinder Kumar, Ibrahim Ben-Daya, Kanav Vats, Jeffery Feng, Graham Taylor and, Alexander Wong
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability.
no code implementations • 15 Jan 2019 • Vignesh Sankar, Devinder Kumar, David A. Clausi, Graham W. Taylor, Alexander Wong
Conclusion: The SISC radiomic sequencer is able to achieve state-of-the-art results in lung cancer prediction, and also offers prediction interpretability in the form of critical response maps.
no code implementations • 29 Oct 2017 • Devinder Kumar, Graham W. Taylor, Alexander Wong
Conclusion: We demonstrate the effectiveness and utility of the proposed CLEAR-DR system of enhancing the interpretability of diagnostic grading results for the application of diabetic retinopathy grading.
no code implementations • 5 Sep 2017 • Devinder Kumar, Graham W. Taylor, Alexander Wong
However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision making processes.
no code implementations • 13 Apr 2017 • Devinder Kumar, Alexander Wong, Graham W. Taylor
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input.
no code implementations • 19 Nov 2016 • Devinder Kumar, Vlado Menkovski, Graham W. Taylor, Alexander Wong
One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions.
no code implementations • 11 Nov 2015 • Mohammad Javad Shafiee, Audrey G. Chung, Devinder Kumar, Farzad Khalvati, Masoom Haider, Alexander Wong
In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data.
no code implementations • 1 Sep 2015 • Devinder Kumar, Mohammad Javad Shafiee, Audrey G. Chung, Farzad Khalvati, Masoom A. Haider, Alexander Wong
In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data.
no code implementations • 1 Sep 2015 • Audrey G. Chung, Mohammad Javad Shafiee, Devinder Kumar, Farzad Khalvati, Masoom A. Haider, Alexander Wong
In this study, we propose a novel \textit{discovery radiomics} framework for generating custom radiomic sequences tailored for prostate cancer detection.
1 code implementation • IEEE 2015 • Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, Frederic Precioso
We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion.