Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond

Khan, Naimul; Abraham, Nabila; Hon, Marcia; Guan, Ling

doi:10.1109/mipr.2019.00023

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Therefore, it is necessary to consider the presence of 30 input features in order to gain a comprehensive understanding of constructing a classification prediction model. Furthermore, it may be argued that a well-balanced and non-skewed distribution of class labels contributes to the favorable convergence of loss in ML algorithms, ultimately leading to the attainment of appropriate solutions [35]. According to recent reports, evidence suggests that an increased number of independent features contribute to the development of ML models that exhibit a reasonable level of accuracy when fitting the dataset [36].…”

Section: Proposed Methodologymentioning

confidence: 99%

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Almufareh,

Tariq,

Humayun

et al. 2023

Healthcare

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Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale.

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Section: Proposed Methodologymentioning

confidence: 99%

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Almufareh,

Tariq,

Humayun

et al. 2023

Healthcare

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show abstract

“…Adding auxiliary weights into the formulation to handle class imbalances is also explored, where class-specific auxiliary weights are placed into the MMD formulation in Yan et al (2017), and into the samples in the source and target datasets in Al-Stouhi and Reddy (2016). In Khan et al (2019), a loss function that focuses on classes with low probability is defined, to classify biomedical images with a class imbalance.…”

Section: The Effect Of Class Imbalance On Damage Localisationmentioning

confidence: 99%

Damage localisation using disparate damage states via domain adaptation

Wickramarachchi,

Gardner,

Poole

et al. 2024

DCE

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A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states. Consequently, the collected data can be incomplete, making it difficult to undertake machine learning tasks, to detect or predict the full range of damage states a structure may experience. Transfer learning is a helpful solution, where data from (source) structures containing damage labels can be used to transfer knowledge to (target) structures, for which damage labels do not exist. Machine learning models are then developed that generalize to the target structure. In practical applications, it is unlikely that the source and the target structures contain the same damage states or experience the same environmental and operational conditions, which can significantly impact the collected data. This is the first study to explore the possibility of transfer learning for damage localisation in SHM when the damage states and the environmental variations in the source and target datasets are disparate. Specifically, using several domain adaptation methods, this article localizes severe damage states at a target structure, using labeled information from minor damage states at a source structure. By minimizing the distance between the marginal and conditional distributions between the source and the target structures, this article successfully localizes damage states of disparate severities, under varying environmental and operational conditions. The effect of partial and universal domain adaptation—where the number of damage states in the source and target datasets differ—is also explored in order to mimic realistic industrial applications of these methods.

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“…The authors of Khan et al (2019) show how one can overcome some of the performance limitations of machine learning algorithms when dealing with biomedical images, namely problems such as interactivity, dependence of large datasets, and class imbalance. In particular, the authors address the advantages of turning to iML when performing biomedical image visualization (Direct Volume Rendering).…”

Section: Related Workmentioning

confidence: 99%

Using meta‐learning to predict performance metrics in machine learning problems

et al. 2021

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Machine Learning has been facing significant challenges over the last years, much of which stem from the new characteristics of Machine Learning problems, such as learning from streaming data or incorporating Human feedback into existing datasets and models. In these dynamic scenarios, data change over time and models must adapt. However, new data do not necessarily mean new patterns. The main goal of this paper is to devise a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth it to train it or not. That is, will the model hold significantly better results than the current one? To address this issue we propose the use of metalearning. Specifically, we evaluate two different meta-models, one built for a specific Machine Learning problem, and another built based on many different problems, meant to be a generic meta-model, applicable to virtually any problem. In this paper we focus only on the prediction of the rmse. Results show that it is possible to accurately predict the rmse of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re-training models between 60% to 98%, depending on the problem and on the threshold used.

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Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond

Cited by 9 publications

References 26 publications

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Damage localisation using disparate damage states via domain adaptation

Using meta‐learning to predict performance metrics in machine learning problems

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