78 citations · 177 across the 15 of their papers we have counts for
16 papers
OmniRAS: Standardizing Foundation Model Training and Evaluation in Robot-Assisted Surgery
Leonardo Borgioli, Neil Getty, Wenli Xiu +8
Few foundation models exist for robot-assisted surgery, partly because large robotic-surgery video corpora are difficult to assemble and existing models are evaluated mostly on lap…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Contextual Active Model Selection
Xuefeng Liu, Fangfang Xia, Rick L. Stevens +1
While training models and labeling data are resource-intensive, a wealth of pre-trained models and unlabeled data exists. To effectively utilize these resources, we present an appr…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
Neko: a Library for Exploring Neuromorphic Learning Rules
Zixuan Zhao, Nathan Wycoff, Neil Getty +2
The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking mod…
A cross-study analysis of drug response prediction in cancer cell lines
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash +21
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm deve…
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
Agastya P. Bhati, Shunzhou Wan, Dario Alfè +26
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottle…
Learning Curves for Drug Response Prediction in Cancer Cell Lines
Alexander Partin, Thomas Brettin, Yvonne A. Evrard +9
Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As…
Recurrent and Spiking Modeling of Sparse Surgical Kinematics
Neil Getty, Zixuan Zhao, Stephan Gruessner +2
Robot-assisted minimally invasive surgery is improving surgeon performance and patient outcomes. This innovation is also turning what has been a subjective practice into motion seq…
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response
Yitan Zhu, Thomas Brettin, Yvonne A. Evrard +6
Transfer learning has been shown to be effective in many applications in which training data for the target problem are limited but data for a related (source) problem are abundant…
Towards On-Chip Bayesian Neuromorphic Learning
Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
If edge devices are to be deployed to critical applications where their decisions could have serious financial, political, or public-health consequences, they will need a way to si…
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
Austin Clyde, Tom Brettin, Alexander Partin +6
By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance dru…
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Prasanna Balaprakash, Romain Egele, Misha Salim +5
Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power…
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout
Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
As neural networks have begun performing increasingly critical tasks for society, ranging from driving cars to identifying candidates for drug development, the value of their abili…
Machine Learning for Antimicrobial Resistance
John W. Santerre, James J. Davis, Fangfang Xia +1
Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community. Here we pr…