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Zaccaria GM, Altini N, Mezzolla G, Vegliante MC, Stranieri M, Pappagallo SA, Ciavarella S, Guarini A, Bevilacqua V. SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107966. [PMID: 38091844 DOI: 10.1016/j.cmpb.2023.107966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intelligence (XAI) to stratify prognosis and derive a gene-based signature. METHODS AE was exploited to learn an unsupervised representation of the gene expression (GE) from three publicly available datasets, each with its own technology. Multi-layer perceptron (MLP) was used to classify prognosis from latent representation. GE data were preprocessed as normalized, scaled, and standardized. Four different AE architectures (Large, Medium, Small and Extra Small) were compared to find the most suitable for GE data. The joint AE-MLP classified patients on six different outcomes: overall survival at 12, 36, 60 months and progression-free survival (PFS) at 12, 36, 60 months. XAI techniques were used to derive a gene-based signature aimed at refining the Revised International Prognostic Index (R-IPI) risk, which was validated in a fourth independent publicly available dataset. We named our tool SurvIAE: Survival prediction with Interpretable AE. RESULTS From the latent space of AEs, we observed that scaled and standardized data reduced the batch effect. SurvIAE models outperformed R-IPI with Matthews Correlation Coefficient up to 0.42 vs. 0.18 for the validation-set (PFS36) and to 0.30 vs. 0.19 for the test-set (PFS60). We selected the SurvIAE-Small-PFS36 as the best model and, from its gene signature, we stratified patients in three risk groups: R-IPI Poor patients with High levels of GAB1, R-IPI Poor patients with Low levels of GAB1 or R-IPI Good/Very Good patients with Low levels of GPR132, and R-IPI Good/Very Good patients with High levels of GPR132. CONCLUSIONS SurvIAE showed the potential to derive a gene signature with translational purpose in DLBCL. The pipeline was made publicly available and can be reused for other pathologies.
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Affiliation(s)
- Gian Maria Zaccaria
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy
| | - Nicola Altini
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy.
| | - Giuseppe Mezzolla
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy
| | - Maria Carmela Vegliante
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Marianna Stranieri
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy
| | - Susanna Anita Pappagallo
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Sabino Ciavarella
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Attilio Guarini
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy; Apulian Bioengineering srl, Via delle Violette, 14, Modugno 70026, Italy
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Ali S, Akhlaq F, Imran AS, Kastrati Z, Daudpota SM, Moosa M. The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review. Comput Biol Med 2023; 166:107555. [PMID: 37806061 DOI: 10.1016/j.compbiomed.2023.107555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 08/13/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
In domains such as medical and healthcare, the interpretability and explainability of machine learning and artificial intelligence systems are crucial for building trust in their results. Errors caused by these systems, such as incorrect diagnoses or treatments, can have severe and even life-threatening consequences for patients. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a popular area of research, focused on understanding the black-box nature of complex and hard-to-interpret machine learning models. While humans can increase the accuracy of these models through technical expertise, understanding how these models actually function during training can be difficult or even impossible. XAI algorithms such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) can provide explanations for these models, improving trust in their predictions by providing feature importance and increasing confidence in the systems. Many articles have been published that propose solutions to medical problems by using machine learning models alongside XAI algorithms to provide interpretability and explainability. In our study, we identified 454 articles published from 2018-2022 and analyzed 93 of them to explore the use of these techniques in the medical domain.
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Affiliation(s)
- Subhan Ali
- Department of Computer Science, Norwegian University of Science & Technology (NTNU), Gjøvik, 2815, Norway.
| | - Filza Akhlaq
- Department of Computer Science, Sukkur IBA University, Sukkur, 65200, Sindh, Pakistan.
| | - Ali Shariq Imran
- Department of Computer Science, Norwegian University of Science & Technology (NTNU), Gjøvik, 2815, Norway.
| | - Zenun Kastrati
- Department of Informatics, Linnaeus University, Växjö, 351 95, Sweden.
| | | | - Muhammad Moosa
- Department of Computer Science, Norwegian University of Science & Technology (NTNU), Gjøvik, 2815, Norway.
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Gedefaw L, Liu CF, Ip RKL, Tse HF, Yeung MHY, Yip SP, Huang CL. Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders. Cells 2023; 12:1755. [PMID: 37443789 PMCID: PMC10340428 DOI: 10.3390/cells12131755] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.
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Affiliation(s)
- Lealem Gedefaw
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chia-Fei Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Rosalina Ka Ling Ip
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Hing-Fung Tse
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chien-Ling Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
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Sulaiman A, Kaur S, Gupta S, Alshahrani H, Reshan MSA, Alyami S, Shaikh A. ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images. Diagnostics (Basel) 2023; 13:2121. [PMID: 37371016 DOI: 10.3390/diagnostics13122121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/17/2023] [Accepted: 06/18/2023] [Indexed: 06/29/2023] Open
Abstract
Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children's bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.
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Affiliation(s)
- Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Swapandeep Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Sultan Alyami
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
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5
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Han Z, Huang H, Lu D, Fan Q, Ma C, Chen X, Gu Q, Chen Q. One-stage and lightweight CNN detection approach with attention: Application to WBC detection of microscopic images. Comput Biol Med 2023; 154:106606. [PMID: 36706565 DOI: 10.1016/j.compbiomed.2023.106606] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/01/2023] [Accepted: 01/22/2023] [Indexed: 01/24/2023]
Abstract
White blood cell (WBC) detection in microscopic images is indispensable in medical diagnostics; however, this work, based on manual checking, is time-consuming, labor-intensive, and easily results in errors. Using object detectors for WBCs with deep convolutional neural networks can be regarded as a feasible solution. In this paper, to improve the examination precision and efficiency, a one-stage and lightweight CNN detector with an attention mechanism for detecting microscopic WBC images, and a white blood cell detection vision system are proposed. The method integrates different optimizing strategies to strengthen the feature extraction capability through the combination of an improved residual convolution module, hybrid spatial pyramid pooling module, improved coordinate attention mechanism, efficient intersection over union (EIOU) loss and Mish activation function. Extensive ablation and contrast experiments on the latest public Raabin-WBC dataset verify the effectiveness and robustness of the proposed detector for achieving a better overall detection performance. It is also more efficient than other existing studies for blood cell detection on two additional classic public BCCD and LISC datasets. The novel detection approach is significant and flexible for medical technicians to use for blood cell microscopic examination in clinical practice.
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Affiliation(s)
- Zhenggong Han
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China
| | - Haisong Huang
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China; Information Engineering Institute, Chongqing Vocational and Technical University of Mechatronics, Chongqing, 402760, China.
| | - Dan Lu
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, 550025, China
| | - Qingsong Fan
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China
| | - Chi Ma
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China
| | - Xingran Chen
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China
| | - Qiang Gu
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China
| | - Qipeng Chen
- Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China
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Explainable quantum clustering method to model medical data. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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7
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Abd El-Ghany S, Elmogy M, El-Aziz A. Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm. Diagnostics (Basel) 2023; 13:diagnostics13030404. [PMID: 36766509 PMCID: PMC9913935 DOI: 10.3390/diagnostics13030404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
The immune system's overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model's average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model's average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models.
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Affiliation(s)
- Sameh Abd El-Ghany
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia
- Correspondence: ; Tel.: +966-503524918
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Abd El-Aziz
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia
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Almadhor A, Sattar U, Al Hejaili A, Ghulam Mohammad U, Tariq U, Ben Chikha H. An efficient computer vision-based approach for acute lymphoblastic leukemia prediction. Front Comput Neurosci 2022; 16:1083649. [PMID: 36507304 PMCID: PMC9729282 DOI: 10.3389/fncom.2022.1083649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 11/14/2022] [Indexed: 11/25/2022] Open
Abstract
Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.
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Affiliation(s)
- Ahmad Almadhor
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia,*Correspondence: Ahmad Almadhor
| | - Usman Sattar
- Department of Management Science, Beaconhouse National University, Lahore, Pakistan,Usman Sattar
| | - Abdullah Al Hejaili
- Computer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia
| | - Uzma Ghulam Mohammad
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan
| | - Usman Tariq
- Department of Management Information Systems, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Haithem Ben Chikha
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
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Ladbury C, Zarinshenas R, Semwal H, Tam A, Vaidehi N, Rodin AS, Liu A, Glaser S, Salgia R, Amini A. Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review. Transl Cancer Res 2022; 11:3853-3868. [PMID: 36388027 PMCID: PMC9641128 DOI: 10.21037/tcr-22-1626] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/07/2022] [Indexed: 11/25/2022]
Abstract
Background and Objective Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a "black box". Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the "black box" by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows. Methods We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022. Key Content and Findings In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive. Conclusions Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.
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Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Reza Zarinshenas
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Hemal Semwal
- Departments of Bioengineering and Integrated Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA
| | - Andrew Tam
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Nagarajan Vaidehi
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Andrei S Rodin
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - An Liu
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Scott Glaser
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
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Talha KR, Bandapadya K, Khan MM. Violence Detection Using Computer Vision Approaches. 2022 IEEE WORLD AI IOT CONGRESS (AIIOT) 2022. [DOI: 10.1109/aiiot54504.2022.9817374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Khalid Raihan Talha
- North South University Bashundhara,Department of Electrical & Computer Engineering,Dhaka,Bangladesh,1229
| | - Koushik Bandapadya
- North South University Bashundhara,Department of Electrical & Computer Engineering,Dhaka,Bangladesh,1229
| | - Mohammad Monirujjaman Khan
- North South University Bashundhara,Department of Electrical & Computer Engineering,Dhaka,Bangladesh,1229
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