Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery

Name: Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery | First MICCAI Workshop, AIIIMA 2022, and First MICCAI Workshop, MIABID 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings
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First MICCAI Workshop, AIIIMA 2022, and First MICCAI Workshop, MIABID 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

Siva Teja Kakileti Maria Gabrani Geetha Manjunath Michal Rosen-Zvi Nathaniel Braman Robert G. Schwartz Alejandro F. Frangi Pau-Choo Chung Christopher Weight Vekataraman Jagadish(Editor)

Springer (Publisher)

Published on 19. November 2022

XIII, 188 pages

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978-3-031-19660-7 (ISBN)

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Content

Intro
Preface AIIIMA 2022
Preface MIABID 2022
Organization
Contents
Artificial Intelligence over Infrared Images for Medical Applications
Thermal Radiomics for Improving the Interpretability of Breast Cancer Detection from Thermal Images
1 Introduction
2 Methodology
2.1 Thermal Radiomics
2.2 Classification
3 Experimentation and Results
4 Conclusions
References
Radiomics for Breast IR-Imaging Classification
1 Introduction
2 Breast IR Classification in the Literature
3 Dataset Description
4 Region of Interest Segmentation
5 Radiomic Feature Extraction
6 Classification Methodology
7 Experiments and Results
8 Conclusion
References
Early Thermographic Screening of Breast Abnormality in Women with Dense Breast by Thermal, Fractal, and Statistical Analysis
1 Background
2 Methods
3 Results
3.1 Thermal Feature-Based Analysis
3.2 Fractal Feature-Based Analysis
3.3 Statistical Feature-Based Analysis
4 Discussion
5 Conclusion and Futurescope
References
A Novel Thermography-Based Artificial Intelligence-Powered Solution for Screening Breast Cancer
1 Introduction
1.1 Thermography
1.2 Related Work
1.3 AI-Powered Breast Cancer Prediction Tool by AI Talos
2 Materials and Methods
2.1 Dataset Description
2.2 CNN Methodology
3 Experimental Results
4 Conclusion
References
Thermographic Toothache Screening by Artificial Intelligence
1 Introduction, Review and Objectives
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Non-fever COVID-19 Detection by Infrared Imaging
1 Introduction
2 Materials and Methods
2.1 Infrared Camera Calibration and Precision Assessment
2.2 Standard Data Bank Construction (Phase 1)
2.3 Classification Algorithm
2.4 Prospective Study (Phase 2)
2.5 Statistical Analysis
3 Results
4 Discussion
5 Conclusion
References
Automated Thermal Screening for COVID-19 Using Machine Learning
1 Introduction
2 Dataset
2.1 Thermal Surveillance Dataset
2.2 Augmented Surveillance Dataset
2.3 Lighting Dataset
3 Methodology
3.1 Image Preprocessing
3.2 Face Detection
3.3 Fever Detection
3.4 Mask Classification
4 Experiments and Results
4.1 Face Detection
4.2 Mask Classification
5 Conclusion
References
An Automated Approach for Screening COVID-19 from Thermal Images Using Convolutional Neural Network
1 Introduction
2 Dataset
3 Methodology
3.1 Overview
3.2 YOLOv5 as Mask Detection Module
3.3 Fever Detection Module
4 Results and Discussion
5 Conclusion
References
Infrared Technology for Vascular Abnormality in Finding of Abdominal Aortic Aneurysm
1 Introduction
1.1 Objective
2 Methodology
2.1 Model Setup
2.2 Boundary Conditions
2.3 Physical and Thermal Properties
3 Verification Studies for FSI Analysis
4 Result and Discussions
4.1 Transient FSI Analysis
5 Limitations
6 Conclusion
References
Non-invasive Thermal Imaging for Estimation of the Fecundity of Live Female Onchocerca Worms
1 Introduction
2 Dataset Description
2.1 Study Site and Population
2.2 Imaging Protocol
2.3 Histopathology and Ground truth
3 Methodology
3.1 Data Pre-processing
3.2 Feature Extraction
3.3 Classification
4 Experiments and Results
5 Conclusion
References
Medical Image Assisted Biomarker Discovery
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
1 Introduction
2 Methods
3 Experiments and Results
3.1 Dataset and Implementation Details
3.2 Evaluating Counterfactuals and Discovered Image-Based Markers
3.3 Counterfactual Results
4 Conclusions
References
CoRe: An Automated Pipeline for the Prediction of Liver Resection Complexity from Preoperative CT Scans
1 Introduction
2 Methods
2.1 Liver, Lesion, and Vessel Segmentation
2.2 Topological Analysis of the Liver Vasculature
2.3 Quantitative Imaging Biomarkers for LR Complexity Prediction
3 Experiments
3.1 Datasets and Preprocessing
3.2 Training, Evaluation, and Inference
4 Results
4.1 Quantitative Results
4.2 Qualitative Results
5 Discussion and Conclusion
References
Diffusion Tensor Imaging Biomarkers for Parkinson's Disease Symptomatology
1 Introduction
1.1 Voxel-Based Diffusion Analysis and Voxel-Based Diktiometry
2 Materials and Methods
2.1 Patient Images and Clinical Scores
2.2 Preprocessing
2.3 Convolutional Neural Network
2.4 Diffusion Measures, Sensitivity Maps, and Statistical Processing
3 Results and Discussion
4 Conclusion
References
Prediction of Immune and Stromal Cell Population Abundance from Hepatocellular Carcinoma Whole Slide Images Using Weakly Supervised Learning
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Gene Expression Processing
2.3 Image Preprocessing
2.4 Deep Learning Models
2.5 Attention Map Generation and Statistical Analysis
2.6 Inflammatory Cell Density Map Generation
3 Results
3.1 Unsupervised Hierarchical Clustering of Samples
3.2 Evaluation of Deep Learning Models for the Prediction of Activation of Cell Populations
3.3 Interpretability and Relationships with Immunotherapy-Related Gene Signatures and with Inflammatory Cells
4 Discussion and Conclusion
References
Enhancing Local Context of Histology Features in Vision Transformers
1 Introduction
2 Methods
3 Experiments
4 Conclusion
References
DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence
1 Introduction
2 Materials
3 Methodology
3.1 Cell Detection, Cell Classification and Hotspot Analysis
3.2 DCIS Segmentation Using GAN
3.3 Stromal TIL Scoring Using Artificial Intelligence
3.4 Statistical Analysis
4 Results and Discussion
References
Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy
1 Introduction
2 Methodology
2.1 Pre-training Phase
2.2 BRAF Classification
3 Experimental Setup
3.1 Dataset and Evaluation Metrics
3.2 Experimental Challenges
3.3 Network Training and Computational Environment
4 Results and Discussion
5 Conclusion
References
Correction to: Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy
Correction to: Chapter "Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy" in: S. T. Kakileti et al. (Eds.): Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery, LNCS 13602, https://doi.org/10.1007/978-3-031-19660-7_17
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Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery

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