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Found 19 papers, 6 papers with code
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment
Several error analysis metrics such as the false positive rate (FPR), and the prediction uncertainty are evaluated, and the results are summarized by cancer type and drug mechanism of action (MoA) category.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Cost-Effective Online Contextual Model Selection
In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Converting tabular data into images for deep learning with convolutional neural networks
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging.
Neko: a Library for Exploring Neuromorphic Learning Rules
To address this gap, we present Neko, a modular, extensible library with a focus on aiding the design of new learning algorithms.
A cross-study analysis of drug response prediction in cancer cell lines
To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
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.
Learning Curves for Drug Response Prediction in Cancer Cell Lines
In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes.
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response
Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment.
Recurrent and Spiking Modeling of Sparse Surgical Kinematics
A growing number of studies have used machine learning to analyze video and kinematic data captured from surgical robots.
Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing
We explore three representative lines of research and demonstrate the utility of our methods on a classification benchmark of brain cancer MRI data.
Towards On-Chip Bayesian Neuromorphic Learning
e-prop 1 is a promising learning algorithm that tackles this with Broadcast Alignment (a technique where network weights are replaced with random weights during feedback) and accumulated local information.
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
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 drug response predictions.
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
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
We use these results to demonstrate the feasibility of conducting the inference phase with permanent dropout on spiking neural networks, mitigating the technique's computational and energy burden, which is essential for its use at scale or on edge platforms.
Machine Learning for Antimicrobial Resistance
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.
