Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part I

✍ Scribed by Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani

• Publisher: Springer Nature
• Tongue: English
• Leaves: 757
• Category: Library

✦ Synopsis

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.

✦ Table of Contents

Foreword by General Chairs
Preface
Challenges
ICPR Organization
Contents – Part I
3DHU 2020 - 3D Human Understanding
Workshop on 3D Human Understanding (3DHU)
Organization
3DHU Workshop Chairs
Technical Program Committee
Additional Reviewer
Sponsors
Subject Identification Across Large Expression Variations Using 3D Facial Landmarks
1 Introduction
2 Temporal Deformable Shape Model
3 Experimental Design and Results
3.1 3D Face Databases
3.2 Experimental Design
3.3 Subject Identification Results
3.4 Subject Identification with Occluded Faces
3.5 Comparisons to State of the Art
4 Conclusion
References
3D Human Pose Estimation Based on Multi-Input Multi-Output Convolutional Neural Network and Event Cameras: A Proof of Concept on the DHP19 Dataset
1 Introduction
2 Related Work
2.1 HPE Datasets
2.2 CNN Architectures
3 Materials and Methods
3.1 Dataset
3.2 Preprocessing and Frame Generation
3.3 Baseline: Single-Input Single-Output (SISO) Architecture
3.4 Proposed Approach: Multiple-Input Multiple-Output (MIMO) Architecture
3.5 3D Human Pose Estimation
3.6 Experimental Procedure
4 Results
4.1 Validation Results
4.2 2D Pose Estimation
4.3 3D Pose Estimation
5 Conclusions
References
Image-Based Out-of-Distribution-Detector Principles on Graph-Based Input Data in Human Action Recognition
1 Introduction
2 Related Work
3 Out-of-Distribution Detectors
3.1 Baseline
3.2 Out-of-DIstribution Detector for Neural Networks
3.3 Learning Confidence for OoD Detection
3.4 Metric Learning-Based Approach
4 Evaluation
4.1 Pipeline
4.2 Semi-synthetic Dataset
4.3 Metrics
4.4 Experimental Setup
4.5 Results
5 Conclusion
References
A Novel Joint Points and Silhouette-Based Method to Estimate 3D Human Pose and Shape
1 Introduction
2 Related Work
3 Method
3.1 Parametric Human Body Model
3.2 Pose Fitting
3.3 Shape Fitting
3.4 Optimization
4 Experiments
4.1 Datasets
4.2 Evaluation of Pose Fitting and Shape Fitting
4.3 Comparison to Previous Approaches
5 Conclusion
References
Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks
1 Introduction
1.1 Motivation
1.2 Related Work
1.3 Main Contributions and Outline of This Article
2 Method
2.1 Data Acquisition and Preprocessing
2.2 Network Architecture
2.3 Evaluation Method
3 Experimental Results
3.1 Hyperparameter Optimization
3.2 Test Results
4 Conclusions and Future Work
References
Towards Generalization of 3D Human Pose Estimation in the Wild
1 Introduction
2 Related Datasets
3 Proposed 3DBodyTex.Pose Dataset
4 Experimental Evaluation
4.1 Baseline 3D Pose Estimation Approach
4.2 Data Augmentation with 3DBodyTex.Pose
5 Conclusion
References
Space-Time Triplet Loss Network for Dynamic 3D Face Verification
1 Introduction
2 Proposed Static 3D Face Representation
3 Dynamic 3D Face Embedding
4 Experimental Results
4.1 Dataset and Experimental Setup
4.2 Face Verification Results
5 Conclusions and Future Works
References
AIDP - Artificial Intelligence for Digital Pathology
Preface
Organization
General Chairs
Program Committee Chair
Program Committee
Noise Robust Training of Segmentation Model Using Knowledge Distillation
1 Introduction
2 Related Work
2.1 Noise Robust Training
2.2 Knowledge Distillation
3 Method
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Evaluation
5 Conclusion
References
Semi-supervised Learning with a Teacher-Student Paradigm for Histopathology Classification: A Resource to Face Data Heterogeneity and Lack of Local Annotations
1 Introduction
2 Methods
2.1 Datasets
2.2 Teacher/Student Paradigm
2.3 Implementation
3 Results
4 Discussion
5 Conclusion
References
Self-attentive Adversarial Stain Normalization
1 Introduction
2 Related Work
3 Approach
4 Dataset and Implementation
4.1 Dataset
4.2 Network Architecture
4.3 Training Details
5 Results and Evaluation
A Conclusions
B Additional Results
References
Certainty Pooling for Multiple Instance Learning
1 Introduction
2 Related Work
3 Proposed Method
4 Experiments
4.1 Low Evidence Ratio MNIST-Bags
4.2 Camelyon16 Lymph Node Metastasis Detection Challenge
5 Conclusions
References
Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing
1 Introduction
2 Related Work
3 Methods
3.1 Corpus
3.2 Corpus Preprocessing
3.3 Data Augmentation
3.4 Document Representation
3.5 Document Classification
3.6 Experimental Setup
4 Results
5 Discussion and Analysis
6 Conclusions and Future Work
References
AI Slipping on Tiles: Data Leakage in Digital Pathology
1 Introduction
2 Data Description
3 Methods
4 Results
5 Discussion
6 Conclusions
References
AIHA 2020 - International Workshop on Artificial Intelligence for Healthcare Applications
Preface
Organization
AIHA Chairs
Program Committee
Additional Reviewers
Predictive Medicine Using Interpretable Recurrent Neural Networks
1 Introduction
2 Related Work
3 Background
3.1 Recurrent Neural Networks (RNN)
3.2 Long Short-Term Memory (LSTM)
3.3 LSTM with Varying Timestamps
3.4 SHAP Values
4 Methodology
4.1 Data
4.2 Data Utils Package
4.3 Interpretability
4.4 Dashboard
4.5 Reproducibility
5 Results
5.1 Model Performance
5.2 Model Interpretation
6 Conclusions
References
Automated Detection of Adverse Drug Events from Older Patients' Electronic Medical Records Using Text Mining
1 Introduction
2 Methods
2.1 De-Identification
2.2 Manual Annotation
2.3 Automatic Annotation
2.4 OGER and BioBERT
2.5 BioBERT
2.6 Harmonisation and Merging
2.7 Drug Administration
3 Results
3.1 De-Identification
3.2 Lexico-Semantic Resources
3.3 Automatic Annotation
4 Discussion
References
Length of Stay Prediction for Northern Italy COVID-19 Patients Based on Lab Tests and X-Ray Data
1 Introduction
2 Related Work
3 Available Data Sources
3.1 Data Quality Issues
4 Datasets for Training and Testing
4.1 Pre-processing and Feature Extraction
4.2 Training and Test Sets Generation
5 Machine Learning Algorithms
5.1 Regression Algorithms
5.2 Hyperparameter Search
6 Experimental Evaluation and Discussion
6.1 Results
7 Conclusions and Future Work
References
Advanced Non-linear Generative Model with a Deep Classifier for Immunotherapy Outcome Prediction: A Bladder Cancer Case Study
1 Introduction
2 Related Works
3 The Proposed Deep Network Framework
3.1 The Bounding Box Segmentation Block
3.2 The 2D-CNN Features Generative Model
3.3 The 2D-DNN Classifier with Decision System
4 Experimental Results
5 Conclusion and Discussion
References
Multi-model Ensemble to Classify Acute Lymphoblastic Leukemia in Blood Smear Images
1 Introduction
2 Prior Art
3 Materials and Methods
3.1 Dataset
3.2 Methodology
4 Results and Discussion
5 Conclusion
References
MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
1 Introduction
2 Proposed Method – MIINet
2.1 The Image Dehazing Module – IDM
2.2 The Image Super-Resolution Module - ISR
3 Experimental Results
3.1 Throat Image Dataset
3.2 Training the IDM
3.3 Training the ISR Module
3.4 The Mean Doctor Opinion Score
3.5 Results
4 Discussion
5 Conclusion
References
Medical Image Tampering Detection: A New Dataset and Baseline
1 Introduction
2 Tampered Medical Image Dataset Generation
3 Framework for Medical Tampering Detection
3.1 Architectures
3.2 Model Parameterization
4 Experiments and Results
4.1 Experimental Setup
4.2 Ablation Study of Backbone Networks
4.3 Results of Our Framework
4.4 Generalizability of the ConnectionNet
5 Conclusion
References
Deep Learning for Human Embryo Classification at the Cleavage Stage (Day 3)
1 Introduction
2 Method
2.1 Images
2.2 State-of-the-Art CNN Model: STORK
2.3 Our CNN Models
2.4 Implantation Rate
3 Results
3.1 Regression Model of Technician Scores for Standalone Cases
3.2 STORK Performance on Middle Slice and Multi-slice Images
3.3 CNN Performance on Middle Slice and Multi-slice Images in Standalone Fashion
3.4 CNN Performance on Individual Technician Scores
3.5 Batch Effect
3.6 Performance on Easy Decisions
3.7 Pregnancy Outcomes
4 Discussion and Conclusion
References
Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
1 Introduction
2 Methodology
2.1 Double Autoencoders
2.2 Pretrained Encoders
2.3 Decoders
2.4 Training Details
3 Experimental Results
3.1 Data and Evaluation Metrics
3.2 Autoencoders vs. Double Autoencoders
3.3 Comparison with Recent Techniques
3.4 Qualitative Analysis
4 Discussion and Conclusion
References
A Superpixel-Wise Fully Convolutional Neural Network Approach for Diabetic Foot Ulcer Tissue Classification
1 Introduction
2 Related Work
3 Proposed Method
3.1 Image Acquisition and Data Annotation
3.2 Ulcer Segmentation and Superpixels Extraction
3.3 Superpixel-Based Tissue Classification
4 Results
4.1 Performance Metrics
4.2 Experimental Results
5 Conclusion
References
Fully vs. Weakly Supervised Caries Localization in Smartphone Images with CNNs
1 Introduction
1.1 Medical Motivation
1.2 Technical Motivation
1.3 Contributions
2 Related Work
3 Methods
3.1 Fully Supervised Object Detection
3.2 Weakly Supervised Localization
3.3 Implementation
4 Experiments
4.1 Data
4.2 Evaluation
5 Discussion
6 Conclusion
References
Organ Segmentation with Recursive Data Augmentation for Deep Models
1 Introduction
2 Dataset
3 Proposed Methodology
4 Experimental Setup
5 Results and Discussion
6 Conclusion
References
Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Pre-processing
2.3 Pre-trained CNNs
2.4 Fine-Tuning
2.5 Fusion
2.6 Evaluation
2.7 Implementation
3 Results
4 Discussion
5 Conclusions
References
Active Surface for Fully 3D Automatic Segmentation
1 Introduction
2 Materials and Methods
2.1 Patient Dataset and PET Protocol Acquisition
2.2 Overview of the Proposed System
2.3 Performance Evaluation
3 Results
4 Discussions
References
Penalizing Small Errors Using an Adaptive Logarithmic Loss
1 Introduction
2 Adaptive Logarithmic Loss
3 Evaluation
4 Results
5 Conclusion
References
Exploiting Saliency in Attention Based Convolutional Neural Network for Classification of Vertical Root Fractures
1 Introduction
2 Relate Work
2.1 VRFs Recognition
2.2 CNN Based Image Classification
2.3 Weakly Supervised Learning
3 Materials and Method
3.1 VRFs DataSets
3.2 Feature Pyramids Attention Convolutional Neural Network
4 Experiments
References
UIP-Net: A Decoder-Encoder CNN for the Detection and Quantification of Usual Interstitial Pneumoniae Pattern in Lung CT Scan Images
1 Introduction
2 State of the Art
2.1 Deep Learning and Convolutional Neural Networks
3 Data and Methods
3.1 Data
3.2 Methods
4 Experimental Setup and Results
4.1 Experimental Setup
4.2 Results
4.3 Discussion
5 Conclusion
References
Don’t Tear Your Hair Out: Analysis of the Impact of Skin Hair on the Diagnosis of Microscopic Skin Lesions
1 Introduction
2 Related Work for Hair Detection
3 Methods
3.1 Hair Segmentation
3.2 Augmentations
3.3 Skin Lesion Classification
4 Results
4.1 Hair Segmentation
4.2 Skin Lesion Classification
5 Conclusions and Future Work
References
Deep Learning Based Segmentation of Breast Lesions in DCE-MRI
1 Introduction
2 State of the Art
3 Materials and Methods
3.1 Data
3.2 Pre-processing
3.3 Segmentation Algorithm
4 Results and Discussion
4.1 Experiment 1: Threshold
4.2 Experiment 2: Optimizer
4.3 Experiment 3: Loss Function
4.4 Experiment 4: Patch Size
4.5 Implementation Details
4.6 Discussion
5 Conclusions and Future Works
References
Fall Detection and Recognition from Egocentric Visual Data: A Case Study
1 Introduction
2 Related Works
2.1 Fall Detection by Fixed Visual Sensors
2.2 Fall Detection by Wearable Devices
2.3 Fall Detection by Wearable Cameras
3 Data Set
4 Method
4.1 Extraction of Frames
4.2 Feature Extraction
4.3 Fusion of Features and Classification Model
5 Experiments and Results
5.1 Experimental Design
5.2 Performance Measurements
5.3 Results
6 Discussion
7 Conclusions
References
Deep Attention Based Semi-supervised 2D-Pose Estimation for Surgical Instruments
1 Introduction
2 Related Work
3 Methodology
3.1 Network Architecture
3.2 Post-processing
3.3 Total Variation as a Confidence Measure for Pose Estimation
3.4 Training Details
4 Experiments
4.1 Datasets
4.2 Results Using RMIT Dataset
4.3 Results Using Endovis Dataset
5 Conclusion
References
Development of an Augmented Reality System Based on Marker Tracking for Robotic Assisted Minimally Invasive Spine Surgery
1 Introduction
2 Methods
2.1 Marker System Selection
2.2 Server-Client Communication
2.3 Evaluation Protocol
3 Results
3.1 Localization Accuracy of the Pose Estimation
3.2 Runtime
3.3 Robustness to External Influences
3.4 Communication and 3D Visualization
4 Discussion
5 Conclusion
References
Towards Stroke Patients' Upper-Limb Automatic Motor Assessment Using Smartwatches
1 Introduction
2 State of the Art
3 Experimentation Protocol
4 Methodology
4.1 Data Capture and Preprocessing
4.2 Data Labelling
4.3 Segmentation
4.4 Classification
5 Results
5.1 Dataset
5.2 Results
6 Conclusion
References
Multimodal Detection of Tonic–Clonic Seizures Based on 3D Acceleration and Heart Rate Data from an In-Ear Sensor
1 Introduction
2 Related Work
3 Method
3.1 Data Preparation
3.2 Learning Process
4 Experiments
4.1 Dataset
4.2 Experimental Setup
4.3 Results
4.4 Discussion
5 Conclusion and Future Work
References
Deep Learning Detection of Cardiac Akinesis in Echocardiograms
1 Introduction
2 Related Works
3 Proposed Method
3.1 Dataset
3.2 Network Specifications
4 Experimental Results
5 Conclusions
References
Prediction of Minimally Conscious State Responder Patients to Non-invasive Brain Stimulation Using Machine Learning Algorithms
1 Introduction
1.1 Methods
2 Results
2.1 Pre-EEG (Stimulation and Sham)
2.2 Pre-EEG (Stimulation and Sham) Augmented with Bootstrap
2.3 Discussion
3 Conclusions
References
Sinc-Based Convolutional Neural Networks for EEG-BCI-Based Motor Imagery Classification
1 Introduction
2 Sinc Layer
3 The Sinc-EEGNet Architecture
4 Experiments
5 Results
6 Conclusions
References
An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting
1 Introduction
2 Classic Velocity-Based Features
3 Additional Kinematic-Based Features
3.1 Maxwell-Boltzmann Distribution
3.2 Discrete Transformations
4 Experiment
5 Conclusions
References
A Comparative Study on Autism Spectrum Disorder Detection via 3D Convolutional Neural Networks
1 Introduction
2 Related Work
2.1 ASD Detection
2.2 3D Convolutional Neural Networks (CNN)
3 3D CNN for ASD
3.1 C3D
3.2 I3D
3.3 3D ResNet
3.4 The Proposed 3D ResNeSt
3.5 Dataset
3.6 Data Preprocessing
4 Experiments and Results
4.1 Implementation Details
4.2 Experimental Results
5 Conclusion
References
A Multi Classifier Approach for Supporting Alzheimer's Diagnosis Based on Handwriting Analysis
1 Introduction
2 Related Work
3 The Multi Classifier Architecture
4 Experimental Results
4.1 Implementation Details
4.2 Dataset
4.3 Tasks Characterisation
4.4 Baseline Evaluation Session
4.5 The Basic Classifiers
4.6 Combining All
4.7 Combining the Best
5 Conclusions
References
A Lightweight Spatial Attention Module with Adaptive Receptive Fields in 3D Convolutional Neural Network for Alzheimer’s Disease Classification
1 Introduction
2 Related Work
2.1 Deep Learning Methods for AD Classification
2.2 Attention Mechanism
2.3 Dilated Convolution
3 Method
3.1 3D Spatial Attention Module with Adaptive Receptive Fields
3.2 Data and Preprocessing
3.3 Experiment Setup
4 Result and Discussion
4.1 Comparisons Using Different Single-Branch Cases
4.2 Comparisons Using Different Two-Branch Cases
4.3 Comparisons Using Different Single-Branch and Two-Branch Cases
4.4 Comparisons with Related Studies
5 Conclusion
References
Handwriting-Based Classifier Combination for Cognitive Impairment Prediction
1 Introduction
2 The Tasks
3 Data Collection and Feature Extraction
4 The Proposed Approach
5 Experiments and Results
6 Conclusions
References
CADL2020 - Workshop on Computational Aspects of Deep Learning
Preface
Organization
General Chairs
Program Committee
WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras
1 Introduction
2 Background
2.1 Wavelet Transform
2.2 TensorFlow and Keras
3 Related Work
3.1 PyWavelets
3.2 pypwt
3.3 TF-Wavelets
4 Implementation
4.1 Direct Transform
4.2 Inverse Transform
4.3 Correctness
5 Performance Results
5.1 Raw Transformation
5.2 Machine Learning
6 Software Availability
7 Conclusion and Future Work
References
Convergence Dynamics of Generative Adversarial Networks: The Dual Metric Flows
1 Introduction
1.1 Motivation: W-GANs
1.2 Mathematical Setting
2 Theoretical Results
2.1 Motivation and Literature Review
2.2 Basic Reminders
2.3 Definition of (EDI Style) Equilibrium Flows
2.4 Convergence of Numerical Schemes
3 Applications
4 Discussion and Conclusion
References
Biomedical Named Entity Recognition at Scale
1 Introduction
2 NER Model Implementation in Spark NLP
3 Implementation Details and Experimental Results
3.1 Datasets
3.2 Overview of Experimental Setup
3.3 Experiment Results
4 Conclusion
A Appendices
References
PyraD-DCNN: A Fully Convolutional Neural Network to Replace BLSTM in Offline Text Recognition Systems
1 Introduction
2 Related Work
3 PyraD-DCNN Model
3.1 Design Principles
3.2 Overall Architecture
4 Experiments
4.1 Data
4.2 Implementation Details
4.3 Ablation Study
5 Results
5.1 Experiment on Small-ID
5.2 Experiment on Big-ID
5.3 Discussion
6 Conclusion
References
Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm
1 Introduction
2 Related Work
2.1 Network Pruning
2.2 Weight Sparsity
3 Training with Kernel-Sparsity
3.1 Kernel-Sparsity Regularization
3.2 Training with Kernel-Sparsity Regularization
3.3 Setting Kernels to Zero
4 Experiments
4.1 Experiments on LeNet
4.2 VGG on CIFAR10
5 Conclusion
References
Multi-node Training for StyleGAN2
1 Introduction
2 Multi-node Training via Horovod
2.1 Process Parallelism
2.2 Data Sharding
2.3 Gradient Averaging
2.4 Multi-node Metrics
3 Validation
4 Scaling Tests
4.1 Strong Scaling
4.2 Weak Scaling
5 Conclusion
References
Flow R-CNN: Flow-Enhanced Object Detection
1 Introduction
2 Related Work
2.1 Region-Based Methods
2.2 Regression-Based Methods
2.3 Flow-Based Object Detection
3 Flow R-CNN
3.1 Object-Based Motion Analysis
3.2 Mask R-CNN
3.3 Proposed Architecture
4 Experimental Results
4.1 Object Detection Datasets
4.2 Experimental Environment
4.3 Comparative Evaluation
5 Conclusions
References
Compressed Video Action Recognition Using Motion Vector Representation
1 Introduction
2 Related Works
3 Proposed Approach
3.1 Motion Vector
3.2 Key Information Selection
3.3 Motion Vector Representation
3.4 Baseline Model
4 Experiments
4.1 Datasets and Experimental Details
4.2 Accuracy and Efficiency
4.3 Ablation Studies
4.4 Visualizations
5 Conclusions
References
Introducing Region Pooling Learning
1 Introduction
2 Related Work
3 Region Pooling Learning
4 Experiments and Results
4.1 Average-Max Pooling Behavior
4.2 CIFAR-10
4.3 ImageNet
5 Conclusion
References
Second Order Bifurcating Methodology for Neural Network Training and Topology Optimization
1 Introduction
2 Related Work
3 Method
3.1 Horizontal Tangent Parabola (HTP)
3.2 Vertical Tangent Parabola (VTP)
3.3 Algorithm
3.4 Example 1
3.5 Example 2
4 Training a Radial Basis Function NN
5 Experiments
5.1 Interpolation of a 2D Surface
5.2 Learning the Kernel Surface
5.3 Application in Convolutional Neural Networks
6 Conclusions
References
Author Index