Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

Vasanthakumari, Priyanka; Romano, Renan Arnon; Rosa, Ramon Gabriel Teixeira; Sálvio, Ana Gabriela; Yakovlev, Vladislav V.; Kurachi, Cristina; Hirshburg, Jason M.; Jo, Javier A.

doi:10.1117/1.jbo.27.6.066002

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…This is a straightforward method where the sole purpose of using ensemble models is to estimate prediction uncertainty. Ensemble learning has been used commonly for improving the prediction accuracy by combining prediction results generated by models trained using different data partitions [3] and/or feature subsets/modalities [56,57]. The results generated from the multiple models within the ensemble are fused [58][59][60] using voting mechanisms, such as simply taking the average, to produce final prediction outcomes [58,59,61].…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Vasanthakumari,

Zhu,

Brettin

et al. 2024

Cancers

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It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Vasanthakumari,

Zhu,

Brettin

et al. 2024

Cancers

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“…In addition to oral cancer diagnosis and delineation, our methodology can have relevance for the development of data-driven tools for other medical applications, as a common requirement is the access to comprehensive health data collected from different institutions, using diverse instrumentation. For example, our group is also developing a FLIM dermoscopy system for early detection and margin assessment of malignant skin lesions [53]. Our methodology can also be applied in very different medical diagnosis tasks that share a similar data type.…”

Section: Discussionmentioning

confidence: 99%

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis

Caughlin

Duran-Sierra

Cheng

et al. 2023

IEEE J. Biomed. Health Inform.

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Deep learning approaches for medical image analysis are limited by small data set size due to multiple factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers.In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network model and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer aided diagnostic systems and presents a feasible approach for creating a single classifier that robustly diagnoses images from multiple data centers in the presence of domain shifts.

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“…For tissue, imaging it in vivo means bringing the hardware into a medical setting which generally implies significant restrictions that are harder to meet during an experimental study. However, a few groups have been working on in vivo applications (Jo [ 136 , 137 , 141 , 142 ]), (Marcu [ 3 , 10 , 109 ]), Cosci et al [ 139 ] and Butte et al [ 147 ]. Except for Butte et al who worked on brain cancer, all the other diseases investigated targeted the skin or the head and neck region, such as oral cancer.…”

Section: Fluorescence Lifetime Image Analysesmentioning

confidence: 99%

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

Gouzou,

Taimori,

Haloubi

et al. 2023

Methods Appl. Fluoresc.

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Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks.Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources.In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics.However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm.In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

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Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

Cited by 6 publications

References 52 publications

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

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