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 / edited by 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.

Format:

Book

Contributor:

Kakileti, Siva Teja, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13602

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Machine learning.

Education--Data processing.

Education.

Social sciences--Data processing.

Social sciences.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Machine Learning.

Computers and Education.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Machine Learning.

Computers and Education.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (200 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

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 MIABID 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 AIIIMA 2022, 10 papers from 15 submissions were accepted for publication. The first workshop on AIIIMA aimed to create a forum to discuss this specific sub-topic of AI over Infrared Images for Medical Applications at MICCAI and promote this novel area of research that has the potential to hugely impact our society, among the research community.

Contents:

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

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

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

A Novel Thermography-Based Artificial Intelligence-Powered Solution for Screening Breast Cancer

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

Thermographic Toothache Screening by Artificial Intelligence

1 Introduction, Review and Objectives

5 Conclusion

Non-fever COVID-19 Detection by Infrared Imaging

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

Automated Thermal Screening for COVID-19 Using Machine Learning

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

An Automated Approach for Screening COVID-19 from Thermal Images Using Convolutional Neural Network

3.1 Overview

3.2 YOLOv5 as Mask Detection Module

3.3 Fever Detection Module

4 Results and Discussion

Infrared Technology for Vascular Abnormality in Finding of Abdominal Aortic Aneurysm

1.1 Objective

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

Non-invasive Thermal Imaging for Estimation of the Fecundity of Live Female Onchocerca Worms

2 Dataset Description

2.1 Study Site and Population

2.2 Imaging Protocol

2.3 Histopathology and Ground truth

3.1 Data Pre-processing

3.2 Feature Extraction

3.3 Classification

Medical Image Assisted Biomarker Discovery

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

3 Experiments and Results

3.1 Dataset and Implementation Details

3.2 Evaluating Counterfactuals and Discovered Image-Based Markers.

3.3 Counterfactual Results

CoRe: An Automated Pipeline for the Prediction of Liver Resection Complexity from Preoperative CT Scans

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

Diffusion Tensor Imaging Biomarkers for Parkinson's Disease Symptomatology

1.1 Voxel-Based Diffusion Analysis and Voxel-Based Diktiometry

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

Prediction of Immune and Stromal Cell Population Abundance from Hepatocellular Carcinoma Whole Slide Images Using Weakly Supervised Learning

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

3.1 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

Enhancing Local Context of Histology Features in Vision Transformers

References.

DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence

2 Materials

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

Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy

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

Author Index.

Other Format:

Print version: Kakileti, Siva Teja Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery

ISBN:

9783031196607

3031196600