A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet

Çifçi, Mehmet Akif

doi:10.3390/diagnostics13040800

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…In this regard, mathematical and statistical models have been used to describe the mechanisms and dynamics of biological experimental findings and the degree of uncertainty quantified. Several deep learning models that quantify uncertainty in the classification results have been proposed including ( Arco et al, 2023 ; Cifci 2023 ; Ren et al, 2023 ). Therefore, collaboration between computational biology experts who develop the prediction models with medical professionals to test the proposed models in real clinical scenarios is highly recommended.…”

Section: Multi-omics Data Integration Interpretation and Disease Pred...mentioning

confidence: 99%

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Wekesa

Kimwele

2023

Front. Genet.

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Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

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Section: Multi-omics Data Integration Interpretation and Disease Pred...mentioning

confidence: 99%

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Wekesa

Kimwele

2023

Front. Genet.

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“…The average amount of annual water per person in Turkey has witnessed a decline, from 1652 m 3 in 2000 to 1346 m 3 in 2020, indicating a nation grappling with water stress [37,38]. To overcome these challenges, conducting studies that maximize the utilization of existing water resources becomes imperative.…”

Section: Related Workmentioning

confidence: 99%

“…The integration of advanced analytical techniques and machine learning algorithms serves as a beacon of hope, aiming to optimize water resource management and tackle challenges related to river flow prediction, water usage efficiency, and environmental sustainability. By delving deeper into these methodologies, we can foster effective water management practices, ensuring water availability for the well-being of future generations [37].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Akbulut,

Cifci,

Aslan

2023

Applied Sciences

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In the face of mounting global challenges stemming from population growth and climate fluctuations, the sustainable management of water resources emerges as a paramount concern. This scientific endeavor casts its gaze upon the Upper Euphrates basin, homing in on the Tunceli Munzur water sub-basin and the Sakarya Basin’s Kütahya Porsuk Stream Beşdeğirmen rivers. The investigation unfolds through the intricate analysis of daily average flow data, total daily precipitation, and daily average air temperature values, with the objective of unraveling the complexities of future water potential estimation. Central to our exploration are a series of well-established techniques including linear regression (LR), support vector regression (SVR), decision tree (DT), random forest (RF), and extra trees regression (ETR). We employ these methodologies diligently to decipher patterns woven within the dataset, fostering an informed understanding of water dynamics. To ascend the pinnacle of estimation accuracy, we introduce a groundbreaking hybrid approach, wherein the enigmatic wavelet transform (WT) technique assumes a pivotal role. Through systematic stratification of our dataset into training, validation, and test sets, comprising roughly 65%, 15%, and 20% of the data, respectively, a comprehensive experiment takes shape. Our results unveil the formidable performance of the ETR method, achieving a striking 88% estimation accuracy for the Porsuk Stream Beşdeğirmen, while the RF method garners a commendable 85.2% success rate for the Munzur water Melekbahçe. The apex of innovation unfolds within our hybrid model, a harmonious fusion of methodologies that transcends their individual capacities. This composite entity elevates estimation success rates by a remarkable 20% for the Munzur water Melekbahçe and an appreciable 11% for the Porsuk Stream Beşdeğirmen. This amalgamation culminates in an extraordinary overall success rate of 97.7%. Our findings transcend mere insights, resonating as guiding beacons for navigating the intricate maze of water resource management in an era marked by uncertainties. This study underscores the indispensability of advanced mathematical paradigms and machine learning frontiers, fortifying the bedrock of sustainable water resource management for the generations to come. By harnessing the fusion of federated learning and a constellation of innovative techniques, we endeavor to illuminate the path towards deciphering the complex tapestry of water resource estimation and management, facilitating a resilient and enduring aquatic world.

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“…However, there are currently few percutaneous puncture procedure path planning systems specifically designed for pulmonary masses. Most systems require interaction with clinicians for semi-automatic path selection (Cifci 2023, Mkindu et al 2023, Shang et al 2023.…”

Section: Introductionmentioning

confidence: 99%

Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints and fuzzy optimization

Zhang,

Han

et al. 2024

Phys. Med. Biol.

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Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience. Approach. To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning. Main results. To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs. Significance. The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure.

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A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet

Cited by 9 publications

References 41 publications

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints and fuzzy optimization

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