Mitigating bias in radiology machine learning: 2. Model development
There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine …
models as they are put into clinical practice. Among the steps for implementing machine …
Interpretable machine learning–a brief history, state-of-the-art and challenges
We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
General pitfalls of model-agnostic interpretation methods for machine learning models
An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
How to avoid machine learning pitfalls: a guide for academic researchers
MA Lones - arXiv preprint arXiv:2108.02497, 2021 - arxiv.org
This document is a concise outline of some of the common mistakes that occur when using
machine learning, and what can be done to avoid them. Whilst it should be accessible to …
machine learning, and what can be done to avoid them. Whilst it should be accessible to …
Using large language models for hyperparameter optimization
This paper studies using foundational large language models (LLMs) to make decisions
during hyperparameter optimization (HPO). Empirical evaluations demonstrate that in …
during hyperparameter optimization (HPO). Empirical evaluations demonstrate that in …
Challenges and best practices in omics benchmarking
TG Brooks, NF Lahens, A Mrčela, GR Grant - Nature Reviews Genetics, 2024 - nature.com
Technological advances enabling massively parallel measurement of biological features—
such as microarrays, high-throughput sequencing and mass spectrometry—have ushered in …
such as microarrays, high-throughput sequencing and mass spectrometry—have ushered in …
The role of hyperparameters in machine learning models and how to tune them
Hyperparameters critically influence how well machine learning models perform on unseen,
out-of-sample data. Systematically comparing the performance of different hyperparameter …
out-of-sample data. Systematically comparing the performance of different hyperparameter …