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 /

This book constitutes the refereed proceedings of the First Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2022, and the First Workshop on Medical Image Assisted Biomarker Discovery, MIABID 2022, both held in conjunction with MICCAI 2022, Singapore, during...

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Corporate Authors: AIIIMA (Workshop) Singapore),
Other Authors: AIIIMA (Workshop), Kakileti, Siva Teja,, Gabrani, Maria,, Manjunath, Geetha, Rosen-Zvi, Michal,, Braman, Nathaniel,, Schwartz, Robert G., Frangi, Alejandro F., Chung, Pau-Choo,, Weight, Christopher,, Jagadish, Vekataraman,, SpringerLink (Online service), MIABIA (Workshop), International Conference on Medical Image Computing and Computer-Assisted Intervention
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2022.
Physical Description: 1 online resource (xiii, 188 pages) : illustrations (some color).
Series: Lecture notes in computer science ; 13602.
Subjects:
Summary: This book constitutes the refereed proceedings of the First Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2022, and the First Workshop on Medical Image Assisted Biomarker Discovery, MIABID 2022, both held in conjunction with MICCAI 2022, Singapore, during September 18 and 22, 2022. For AIIIMA 2022, 7 papers from 10 submissions were accepted for publication. This workshop created a forum to discuss this specific sub-topic at MICCAI and promote this novel area of research among the research community that has the potential to hugely impact our society. For MIABID 2022, 10 papers from 15 submissions were accepted for publication. This workshop brought together together clinical, AI, regulatory, and pharmaceutical experts to review scientific progress and challenges in the development of AI-powered biomarkers leveraging medical imaging.
Item Description: This book constitutes the refereed proceedings of the First Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2022, and the First Workshop on Medical Image Assisted Biomarker Discovery, MIABID 2022, both held in conjunction with MICCAI 2022, Singapore, during September 18 and 22, 2022. For AIIIMA 2022, 7 papers from 10 submissions were accepted for publication. This workshop created a forum to discuss this specific sub-topic at MICCAI and promote this novel area of research among the research community that has the potential to hugely impact our society. For MIABID 2022, 10 papers from 15 submissions were accepted for publication. This workshop brought together together clinical, AI, regulatory, and pharmaceutical experts to review scientific progress and challenges in the development of AI-powered biomarkers leveraging medical imaging.
Includes author index.
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.
Physical Description: 1 online resource (xiii, 188 pages) : illustrations (some color).
ISBN: 9783031196607
3031196600
9788303119667
8303119664
ISSN: 1611-3349 ;