390 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of OxfordGB9 papers
- Rowan UniversityUS8 papers
- Harvard UniversityUS4 papers
- University of PennsylvaniaUS4 papers
- Chalmers University of TechnologySE3 papers
- Georgetown UniversityUS3 papers
- University of Alabama at BirminghamUS3 papers
- University of GothenburgSE3 papers
- Vanderbilt UniversityUS3 papers
- Baylor College of MedicineUS2 papers
- Brigham and Women's HospitalUS2 papers
- Carnegie Mellon UniversityUS2 papers
8 papers · 1 filter
SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networks
Giuseppina Carannante, Nidhal C. Bouaynaya, Dimah Dera +2
Deep Learning (DL) holds great promise in reshaping the industry owing to its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-dist…
Interpretable Visual Understanding with Cognitive Attention Network
Xuejiao Tang, Wenbin Zhang, Yi Yu +4
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-l…
Improving the efficiency of small animal 3D printed compensator IMRT with beamlet intensity total variation regularization
Xinmin Liu, Alexander L. Van Slyke, Erik Pearson +3
Purpose: There is growing interest in the use of modern 3D printing technology to implement intensity-modulated radiation therapy (IMRT) on the preclinical scale which is analogous…
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool +2
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decis…
Exploring Robust Architectures for Deep Artificial Neural Networks
Asim Waqas, Ghulam Rasool, Hamza Farooq +1
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance. However, the relationship between the architecture of a…
Ten Quick Tips for Deep Learning in Biology
Benjamin D. Lee, Anthony Gitter, Casey S. Greene +17
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that…
Super-Linear Growth Reveals the Allee Effect in Tumors
Youness Azimzade, Abbas Ali Saberi, Robert A. Gatenby
Integrating experimental data into ecological models plays a central role in understanding biological mechanisms that drive tumor progression where such knowledge can be used to de…
Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data
Lingsong Meng, Dorina Avram, George Tseng +1
The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subty…