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1. Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients

   https://doi.org/10.1093/bib/bbaf398  

   Feng, Catherine H; Deng, Fei; Disis, Mary L; Gao, Nan; Zhang, Lanjing (July 2025, Briefings in Bioinformatics)

   Abstract Classification of patient multicategory survival outcomes is important for personalized cancer treatments. Machine learning (ML) algorithms have increasingly been used to inform healthcare decisions, but these models are vulnerable to biases in data collection and algorithm creation. ML models have previously been shown to exhibit racial bias, but their fairness towards patients from different age and sex groups have yet to be studied. Therefore, we compared the multimetric performances of five ML models (random forests, multinomial logistic regression, linear support vector classifier, linear discriminant analysis, and multilayer perceptron) when classifying colorectal cancer patients (n = 589) of various age, sex, and racial groups using The Cancer Genome Atlas data. All five models exhibited biases for these sociodemographic groups. We then repeated the same process on lung adenocarcinoma (n = 515) to validate our findings. Surprisingly, most models tended to perform more poorly overall for the largest sociodemographic groups. Methods to optimize model performance, including testing the model on merged age, sex, or racial groups, and creating a model trained on and used for an individual or merged sociodemographic group, show potential to reduce disparities in model performance for different groups. This is supported by our regression analysis showing associations between model choice and methodology used with reduced performance disparities across demographic subgroups. Notably, these methods may be used to improve ML fairness while avoiding penalizing the model for exhibiting bias and thus sacrificing overall performance. 

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2. Infrared imaging technology for breast cancer detection – Current status, protocols and new directions

   Satish G. Kandlikar, Isaac Perez-Raya (January 2017, International Journal of Heat and Mass Transfer)

   Early and accurate detection of breast cancer is a critical part of the strategy to reduce the morbidity and mortality associated with this common disease. While current guidelines recommend mammography for screening, the sensitivity and specificity of mammograms remains less than optimal, especially for patients with dense breast tissue. Thermography has been explored in the past as an alternative to mammography. Advances in IR cameras that are used to obtain thermal images of the breast as well as computational tools used to accurately model heat transfer within the breast have significantly increased the accuracy of thermography. The current work reviews the progress that has been made in using thermal imaging to detect breast cancer over the past three decades and identifies aspects that need further refinement for it to become a reliable tool to diagnose breast cancer. Recent advances and suggestions for future work in the field including using advanced simulation methods, inverse modeling, imaging protocols, and using artificial neural networks to better predict the location of the tumor are also presented. 

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3. Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset

   https://doi.org/10.1093/bioinformatics/btac267  

   Seo, Hoon; Brand, Lodewijk; Barco, Lucia Saldana; Wang, Hua (June 2022, Bioinformatics)

   Abstract Motivation Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progression, many machine learning models have been developed in recent years to automate the histopathological classification of the different types of carcinomas. However, many of them are not scalable to large-scale datasets. Results In this study, we propose the novel Primal-Dual Multi-Instance Support Vector Machine to determine which tissue segments in an image exhibit an indication of an abnormality. We derive an efficient optimization algorithm for the proposed objective by bypassing the quadratic programming and least-squares problems, which are commonly employed to optimize Support Vector Machine models. The proposed method is computationally efficient, thereby it is scalable to large-scale datasets. We applied our method to the public BreaKHis dataset and achieved promising prediction performance and scalability for histopathological classification. Availability and implementation Software is publicly available at: https://1drv.ms/u/s!AiFpD21bgf2wgRLbQq08ixD0SgRD?e=OpqEmY. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Data augmentation via diffusion model to enhance AI fairness

   https://doi.org/10.3389/frai.2025.1530397  

   Hastings_Blow, Christina; Qian, Lijun; Gibson, Camille; Obiomon, Pamela; Dong, Xishuang (March 2025, Frontiers in Artificial Intelligence)

   IntroductionAI fairness seeks to improve the transparency and explainability of AI systems by ensuring that their outcomes genuinely reflect the best interests of users. Data augmentation, which involves generating synthetic data from existing datasets, has gained significant attention as a solution to data scarcity. In particular, diffusion models have become a powerful technique for generating synthetic data, especially in fields like computer vision. MethodsThis paper explores the potential of diffusion models to generate synthetic tabular data to improve AI fairness. The Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM), a diffusion model adaptable to any tabular dataset and capable of handling various feature types, was utilized with different amounts of generated data for data augmentation. Additionally, reweighting samples from AIF360 was employed to further enhance AI fairness. Five traditional machine learning models—Decision Tree (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)—were used to validate the proposed approach. Results and discussionExperimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification. 

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5. A Stochastic Optimization Framework for Fair Risk Minimization

   Lowy, Andrew; Baharlouei, Sina; Pavan, Rakesh; Razaviyayn, Meisam; Beirami, Ahmad (December 2022, Transactions on machine learning research)

   Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibility of fairness constraints with stochastic optimization. We consider the problem of fair classification with discrete sensitive attributes and potentially large models and data sets, requiring stochastic solvers. Existing in-processing fairness algorithms are either impractical in the large-scale setting because they require large batches of data at each iteration or they are not guaranteed to converge. In this paper, we develop the first stochastic in-processing fairness algorithm with guaranteed convergence. For demographic parity, equalized odds, and equal opportunity notions of fairness, we provide slight variations of our algorithm–called FERMI–and prove that each of these variations converges in stochastic optimization with any batch size. Empirically, we show that FERMI is amenable to stochastic solvers with multiple (non-binary) sensitive attributes and non-binary targets, performing well even with minibatch size as small as one. Extensive experiments show that FERMI achieves the most favorable tradeoffs between fairness violation and test accuracy across all tested setups compared with state-of-the-art baselines for demographic parity, equalized odds, equal opportunity. These benefits are especially significant with small batch sizes and for non-binary classification with large number of sensitive attributes, making FERMI a practical, scalable fairness algorithm. The code for all of the experiments in this paper is available at: https://github.com/optimization-for-data-driven-science/FERMI. 

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