A lightweight neural network with multiscale feature enhancement for liver CT segmentation

Ansari, Mohammed Yusuf; Yang, Yin; Balakrishnan, Shidin; Abinahed, Julien; Al‐Ansari, Abdulla; Warfa, Mohamed; Almokdad, Omran; Barah, Ali; Omer, Ahmed M.; Singh, Ajay Vikram; Meher, Pramod Kumar; Bhadra, Jolly; Halabi, Osama; Azampour, Mohammad Farid; Navab, Nassir; Wendler, Thomas; Dakua, Sarada Prasad

doi:10.1038/s41598-022-16828-6

Cited by 51 publications

(22 citation statements)

References 36 publications

(35 reference statements)

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“…A more equitable approach would be to condense pore-typing into a binary classification problem, such as distinguishing between microfractures and pores, as they represent visually distinct endmembers in morphology and are distinct in the mode of genesis. This framing plays to the strength of most supervised ML classifiers as some were designed to be binary classifiers (multiple logistic regression [MLR] and SVM, among others), and single decision boundaries between two classes are far simpler to construct for any model (Ansari, Yang, et al, 2022;Bishop, 2006;Galar et al, 2011;James et al, 2021;Kuhn & Johnson, 2013;Kuhn & Silge, 2022). In addition, binary classifications also enable additional model performance metrics such as the Receiver Operating Characteristic (ROC) curves (James et al, 2021;Kuhn & Johnson, 2013;Kuhn & Silge, 2022).…”

Section: Introductionmentioning

confidence: 99%

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

Jayachandran,

Gibbs,

Laya

et al. 2024

Earth and Space Science

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Petrographic observations are vital for carbonate pore‐typing, linking geological frameworks to petrophysical behavior. However, current petrographic pore typing is manual, with the qualitative to semi‐quantitative results not easily fitted into quantitative subsurface characterization. Some recent studies have automated this process using supervised machine learning (ML) and deep learning (DL), focusing on simple pore morphological features, and have reported high classification accuracies for several complex pore types. However, there are concerns about the validity of these studies due to conceptual and technical flaws in their collective approach. This study was aimed at a more fundamental problem, classifying between open microfractures and open pores in petrographic thin sections using an object‐based approach and explainable supervised ML. We analyzed 18 carbonate thin sections from the USA, numerically representing them using five shape features: compactness, aspect ratio, extent, solidity, and formfactor. Using a labeled data set of 400 microfractures and 400 pores, we evaluated nine of the most widely used supervised models. All models showed high testing accuracies (89.58%–90.42%). Interestingly, complex non‐linear models did not significantly outperform simpler linear ones. Compactness and aspect ratio were the most informative features. However, the labeled data sets did not reflect the overall data set's complexity, which suggested that high accuracies in similar studies might be due to curated data sets rather than accounting for the true complexity of carbonate pore systems. The study concludes that simple shape features are ineffective for classifying carbonate pore types. It is hoped that this study will provide a foundation for more robust artificial intelligence‐assisted pore typing.

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Section: Introductionmentioning

confidence: 99%

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

Jayachandran,

Gibbs,

Laya

et al. 2024

Earth and Space Science

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“…Predicting and forecasting the BBB transport by using computational models including artificial intelligence (AI) and machine learning (ML) can effectively accelerate the drug development process for neurological conditions [1], [12], [13]. Recent years have seen unprecedented applications of AI/ML methods in addressing diverse problems ranging from medical image analysis [14], [15], [16] to drug discovery [17]. Several AI/ML-based models have been proposed to facilitate expeditious CNS drug discovery/repurposing by minimizing the number of laborious and time-consuming BBB permeability studies [4], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing

et al. 2023

Self Cite

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Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by employing Machine learning models can effectively identify the drug candidates that could be repurposed for neurological conditions. This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related biases. We found that random forest and extratrees (i.e., tree-based ensembled models) have the highest accuracy with mol2vec fingerprint for BBB permeability prediction, attaining AUC_ROC of 0.9453 and 0.9601 on BBB and B3DB dataset, respectively. Additionally, we have analyzed the impact of the data balancing technique (i.e., SMOTE) to improve the specificity of the models. Finally, we have explored the impact of different fingerprint combinations on accuracy. By employing SMOTE and fingerprint combination, SVC attains the highest AUC_ROC of 0.9511 on BBB dataset. Finally, we used the best-performing models of the B3DB dataset to evaluate the BBB permeability for drugs intended to be used for repurposing. Model validation for repurposing predicted the non-passage for most antihypertensive drugs and passage for CYP17A1 cancer drugs.INDEX TERMS Blood brain barrier, drug permeability, drug repurposing, empirical study, machine learning.

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“…Due to the aforementioned applications, food computing has been highlighted as an important research direction from the research community due to its benefits and wide use cases. In recent years, automatic food recognition (AFR) has received renewed attention due to the success of deep learning models in classification tasks of computer vision and multimedia applications [1]- [3]. The benefits and application of AFR is wide and diverse.…”

Section: Introductionmentioning

confidence: 99%

MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East

Ansari

Qaraqe

2023

IEEE Access

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Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is challenging due to its fine-grained nature and its high correlation with culture, geo-location, and language. To make food recognition systems feasible for the Middle Eastern region, we present a large-scale dataset (MEFood) of commonly consumed food items in the Middle East, thereby providing a dataset for current development and establishing a benchmark for future research. We have also thoroughly examined the MEFood dataset highlighting its challenging aspects and its real-world nature. Additionally, we have conducted a thorough experimental study benchmarking the mainstream computer vision and mobile networks on classification, runtime, and resource utilization metrics. Our results highlight that EfficientNet-V2 achieves performance closer to the best-performing individual model on the MEFood dataset while having the least resource utilization and minimal inference times. Finally, we have performed a thorough error analysis study to glean additional insights about the networks and MEFood dataset.

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A lightweight neural network with multiscale feature enhancement for liver CT segmentation

Cited by 51 publications

References 36 publications

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing

MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East

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