Mitigating bias in radiology machine learning: 2. Model development

K Zhang, B Khosravi, S Vahdati, S Faghani… - Radiology: Artificial …, 2022 - pubs.rsna.org
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 …

Visual tuning

BXB Yu, J Chang, H Wang, L Liu, S Wang… - ACM Computing …, 2024 - dl.acm.org
Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Interpretable machine learning–a brief history, state-of-the-art and challenges

C Molnar, G Casalicchio, B Bischl - Joint European conference on …, 2020 - Springer
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 …

On the analyses of medical images using traditional machine learning techniques and convolutional neural networks

S Iqbal, A N. Qureshi, J Li, T Mahmood - Archives of Computational …, 2023 - Springer
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

General pitfalls of model-agnostic interpretation methods for machine learning models

C Molnar, G König, J Herbinger, T Freiesleben… - … Workshop on Extending …, 2020 - Springer
An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …

How interpretable machine learning can benefit process understanding in the geosciences

S Jiang, L Sweet, G Blougouras, A Brenning… - Earth's …, 2024 - Wiley Online Library
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 …

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 …

Using large language models for hyperparameter optimization

MR Zhang, N Desai, J Bae, J Lorraine… - … 2023 Foundation Models …, 2023 - openreview.net
This paper studies using foundational large language models (LLMs) to make decisions
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 …

The role of hyperparameters in machine learning models and how to tune them

C Arnold, L Biedebach, A Küpfer… - … Science Research and …, 2024 - cambridge.org
Hyperparameters critically influence how well machine learning models perform on unseen,
out-of-sample data. Systematically comparing the performance of different hyperparameter …