Intro
Preface
Organization
Contents
Classification
3D-Morphomics, Morphological Features on CT Scans for Lung Nodule Malignancy Diagnosis
1 Introduction
2 Methods
2.1 Data Sets
2.2 Data Analysis Models
3 Results
3.1 3D-Morphomics
3.2 Lung Nodule Diagnosis Performances of 3D-Morphomics
4 Conclusions
References
.26em plus .1em minus .1emSelf-supervised Approach for a Fully Assistive Esophageal Surveillance: Quality, Anatomy and Neoplasia Guidance
1 Introduction
2 Related Work
3 Method
3.1 Self-supervision Solving Jigsaw Puzzle

3.2 Fine-Tuning with Angular Margin Loss
4 Experiments and Results
4.1 Implementation Details
4.2 Data Collection and Evaluation Metrics
4.3 Comparison with SOTA Methods
4.4 Qualitative Analysis
5 Conclusion
References
Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset
1 Introduction
2 Related Works
3 Materials and Methods
3.1 IMGL Dataset Description
3.2 The Proposed IMGL-VTNet Architecture
3.3 Multi-scale Deformable Transformer Encoder
4 Experimental Results

4.1 A Comparison of State-of-the-Art Methods: IMGL Dataset
4.2 Feature Map Scales Analysis
4.3 Application of the Proposed Model to Pedestrian Detection
5 Conclusion
References
Parallel Classification of Cells in Thinprep Cytology Test Image for Cervical Cancer Screening
1 Introduction
2 Method
2.1 Overview
2.2 Dual Classifiers in Parallel
2.3 Intra-class Compactness
2.4 Implementation Details
3 Experimental Results
3.1 Datasets
3.2 Classification Performance
3.3 Evolving of the Latent Space
4 Discussion and Conclusion
References

Detection and Diagnosis
Lightweight Transformer Backbone for Medical Object Detection
1 Introduction
2 Methodology
2.1 Overview of Proposed Method
2.2 Feature Map Rearrangement and Reconstruction
2.3 Lightweight Transformer on Feature Patches
3 Experiments and Results
3.1 Dataset and Evaluation Metrics
3.2 Implementation Details
3.3 Experimental Results
4 Conclusion
References
Contrastive and Attention-Based Multiple Instance Learning for the Prediction of Sentinel Lymph Node Status from Histopathologies of Primary Melanoma Tumours
1 Introduction

2 Materials and Methods
2.1 Dataset
2.2 Multiple Instance Learning
2.3 Proposed Model
2.4 Self-supervised Contrastive Learning:
3 Experimental Set-Up and Results
3.1 Feature Extraction
3.2 Experiments
4 Discussion
5 Conclusions
References
Knowledge Distillation with a Class-Aware Loss for Endoscopic Disease Detection
1 Introduction
2 Related Work
3 Materials and Method
3.1 Datasets
3.2 Proposed Knowledge-Distillation Framework
4 Experiments and Results
4.1 Experimental Setup and Evaluation Metrics
4.2 Results
5 Conclusion
References