Partial dependence of breast tumor malignancy on ultrasound image features derived from boosted trees

Yang, Wei; Zhang, Su; Li, Wenying; Chang, Yaqing; Lu, Hongtao; Chen, Wufan; Chen, Yazhu

doi:10.1117/1.3385763

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Feature importance techniques such as partial dependence plots (PDPs) [82], individual conditional expectation (ICE) plots [83], and SHapley Additive exPlanations (SHAP) [84][85][86] have been used for analysing partial dependency of breast tumor malignancy on ultrasound image features [87], and uncovering important features in predicting cognitive decline in Alzheimer's disease [88].…”

Section: B Visualization Techniquesmentioning

confidence: 99%

A Framework for Interpretability in Machine Learning for Medical Imaging

Wang,

Karaman,

Kim

et al. 2024

IEEE Access

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Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research.

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Section: B Visualization Techniquesmentioning

confidence: 99%

A Framework for Interpretability in Machine Learning for Medical Imaging

Wang,

Karaman,

Kim

et al. 2024

IEEE Access

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QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition

Ancuceanu,

Popovici,

Drăgănescu

et al. 2024

Pharmaceuticals

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Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥ 8 (IC50 values ≤ 10 nM). Our svm-based ensemble model predicted IC50 values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

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Partial dependence of breast tumor malignancy on ultrasound image features derived from boosted trees

Cited by 2 publications

References 29 publications

A Framework for Interpretability in Machine Learning for Medical Imaging

A Framework for Interpretability in Machine Learning for Medical Imaging

QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition

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