Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, ... (Lecture Notes in Computer Science, 14227)

The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinicalapplications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration. Preface Organization Contents – Part VIII Clinical Applications – Neuroimaging CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation 1 Introduction 2 Datasets 3 Method 3.1 Overview 3.2 Multi-head Architecture 3.3 Longitudinal Relation Regularization 4 Results 5 Conclusion References Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model 1 Introduction 2 Methodology 2.1 Diffusion Probabilistic Model 2.2 Conditional Generation with DPM (cDPM) 2.3 Network Architecture 3 Experiments 3.1 Data 3.2 Implementation Details 3.3 Quantitative Comparison 3.4 Results 4 Conclusion References Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer 1 Introduction 2 3D Hybrid Graph Transformer 2.1 Network Overview 2.2 Efficient q-Space Learning Module 2.3 3D x-Space Learning Module 3 Experiments 3.1 Implementation Details 3.2 Dataset and Evaluation Metrics 3.3 Experimental Results 3.4 Ablation Study 4 Conclusion References Cortical Analysis of Heterogeneous Clinical Brain MRI Scans for Large-Scale Neuroimaging Studies 1 Introduction 2 Methods 2.1 Learning of SDFs 2.2 Geometry Processing for Surface Placement 2.3 Implementation Details 3 Experiments and Results 3.1 Datasets 3.2 Competing Methods 3.3 Results on the ADNI Dataset 3.4 Results on the Clinical Dataset 3.5 Discussion and Conclusion References Flow-Based Geometric Interpolation of Fiber Orientation Distribution Functions 1 Introduction 2 Method 2.1 FOD Decomposition 2.2 Modeling Single Peak FOD Components as Flow of Vector Fields 2.3 Rotation Calculation for SPHARM-Based FODs 2.4 Evaluation Methods 3 Experiment Results 4 Conclusion References Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis 1 Introduction 2 Method 2.1 Preliminary 2.2 Functional Subdivision Block 2.3 Functional Aggregation Block 2.4 Objective Function 3 Experiments 3.1 Dataset and Experimental Settings 3.2 Result Analysis 3.3 Ablation Study 3.4 Interpretability of Brain States 4 Conclusion References FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis 1 Introduction 2 Method 2.1 Local Spatial Structural Features and Short-Term Temporal Characteristics Extraction 2.2 Spatio-Temporal Fusion with Dynamic FC and EC 3 Experiments 3.1 Dataset and Experimental Settings 3.2 Ablation Studies 3.3 Comparison with Other Methods 4 Conclusion References Learning Normal Asymmetry Representations for Homologous Brain Structures 1 Introduction 2 Methods 2.1 Pre-training the Shape Characterization Encoder as a CAE 2.2 Learning Normal Asymmetries with a Siamese Network 3 Experimental Setup 4 Results and Discussion 4.1 Characterization of Normal and Disease Related Asymmetries 4.2 Comparison with Other Approaches 5 Conclusions References Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model 1 Introduction 2 Methods 3 Experiments and Results 4 Discussions and Conclusion References Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disorders with Adaptive Graph Meta-Learner 1 Introduction 2 Methods 2.1 Notation and Problem Formulation 2.2 Meta-Learner Training Algorithm 2.3 Multi-view Graph Classifier c 2.4 Meta-Controller m 3 Experiments 3.1 Dataset 3.2 Settings 3.3 Results and Discussions 4 Conclusion References Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI 1 Introduction 2 Materials and Proposed Method 3 Experiment 4 Conclusion and Future Work References Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI 1 Introduction 2 Methods 2.1 Backbone 2.2 Dynamic Hierarchical Prototype Learning 2.3 Brain Network Graph Construction and Classification 3 Experiments 3.1 Dataset 3.2 Implementation Details 4 Results 4.1 Comparing with SOTA Methods 4.2 Ablation Study 5 Conclusion References Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments 1 Introduction 2 Methods 2.1 Fingerprint Dictionary 2.2 Solving the Bloch-Torrey Partial Differential Equation (BT-PDE) 2.3 Solving for Volume Fractions 2.4 Radius Bias Correction 3 Experiments 3.1 Volume Fraction 3.2 Cell Size and Membrane Permeability 3.3 In-vivo Data 3.4 Histological Corroboration 4 Conclusion References AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation 1 Introduction 2 Method 2.1 q-t Space Sparsity 2.2 Adaptive Uncertainty Attention Modelling 2.3 Dataset and Training 3 Experiments and Results 3.1 Ablation Study 3.2 Performance Test 4 Conclusion References Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture 1 Introduction 2 Methods 2.1 Multi-compartment Model 2.2 Model Simplification via Spherical Mean 2.3 Estimation of Relaxation and Diffusion Parameters 2.4 Microstructure Indices 2.5 Data Acquisition and Processing 3 Results 3.1 Ex Vivo Data: Compartment-Specific Parameters 3.2 In Vivo Data: Compartment-Specific Parameters 3.3 In Vivo Data: Neurite Morphology 3.4 Relation Between Relaxation and Diffusivity 3.5 fODFs 4 Conclusion References Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark 1 Introduction 2 Method 2.1 Dataset and Preprocessing 2.2 Joint Representation of Functional and Structural Profiles 2.3 Consistency Analysis from Anatomical, Structural and Functional Perspective 2.4 Comparative Analysis of Consistent 3-hinges for Structural Data and Multimodal Data 3 Result 3.1 Visualization of the Identified Consistent 3-hinges 3.2 Effectiveness of the Proposed Consistent 3-hinges 3.3 Comparative Analysis on the Consistent 3-hinges Based on Structural Data and Multimodal Data 4 Conclusion References Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations 1 Introduction 2 Methods 3 Experiments and Results 4 Discussion and Conclusion References DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data 1 Introduction 2 Methodology 2.1 The DeepSOZ Model Architecture 2.2 Loss Function and Model Training 2.3 Model Validation 3 Experimental Results 4 Conclusion References Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning 1 Introduction 2 Methods 3 Experiments 3.1 Results 3.2 Ablation Studies 3.3 Interpretability 4 Conclusion References BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis 1 Introduction 2 Method 2.1 Graph Generation Module 2.2 Topology-Aware Encoder 2.3 Objective Functions 3 Experiments and Results 3.1 Dataset and Experimental Details 3.2 Classification Results 3.3 Functional Connectivity Analysis 3.4 Association of Brain Diseases 4 Conclusion References Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis 1 Introduction 2 Proposed Method 2.1 Attention-Based Sparse Common-and-Individual FBN Construction Module (ASCFCM) 2.2 Cross Spatiotemporal Asynchronous FCs 3 Experiments 3.1 Data and Preprocessing 3.2 Experimental Settings 3.3 Experimental Results 3.4 Ablation Study 4 Visualization and Conclusion References Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images 1 Introduction 2 Methodology 3 Experiments 4 Results 5 Conclusion References .28em plus .1em minus .1ematTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning 1 Introduction 2 Methods 2.1 Binary Classification for Tract Segmentation 2.2 Active Learning for Tract Selection 3 Experiments 3.1 Data 3.2 Experimental Setup 3.3 Results 4 Discussion References Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI 1 Introduction 2 Methods 2.1 Training Dataset, Preprocessing, and Data Representation 2.2 Domain Randomisation and Data Augmentation 2.3 Loss 2.4 Architecture and Implementation Details 3 Experiments and Results 3.1 MRI Data 3.2 Competing Methods and Ablations 3.3 Results 4 Discussion and Conclusion References Neural Pre-processing: A Learning Framework for End-to-End Brain MRI Pre-processing 1 Introduction 2 Methods 2.1 Model 2.2 Loss Function 3 Experiments 3.1 Runtime Analyses 3.2 Pre-processing Performance 3.3 Ablation 4 Conclusion References Dynamic Functional Connectome Harmonics 1 Introduction 2 Methods 3 Results 4 Discussion 5 Conclusion References Wasserstein Distance-Preserving Vector Space of Persistent Homology 1 Introduction 2 Wasserstein Distance-Preserving Vector Space 2.1 One-Dimensional Persistence Diagrams 2.2 Closed-Form Wasserstein Distance for Different-Size Networks 2.3 Vector Representation of Persistence Diagrams 3 Application to Functional Brain Networks References Community-Aware Transformer for Autism Prediction in fMRI Connectome 1 Introduction 2 Method 2.1 Overview 2.2 Local-Global Transformer Encoder 2.3 Graph Readout Layer 3 Experiments 3.1 Datasets and Experimental Settings 3.2 Quantitative and Qualitative Results 3.3 Ablation Studies 4 Conclusion References Overall Survival Time Prediction of Glioblastoma on Preoperative MRI Using Lesion Network Mapping 1 Introduction 2 Materials and Methods 2.1 Materials 2.2 Methods 3 Experiments and Results 3.1 Experimental Settings 3.2 Comparison Studies 3.3 Brain Regions in Relation to GBM Survival 4 Conclusion References Exploring Brain Function-Structure Connectome Skeleton via Self-supervised Graph-Transformer Approach 1 Introduction 2 Method 2.1 Overview 2.2 Data and Preprocess 2.3 TSGR Framework 2.4 Analyzing Brain Key Connectome ROIs and Hierarchical Networks 2.5 Exploring the Connectome Skeleton of Brain ROIs 3 Experiments and Results 3.1 Experimental Performance 3.2 Analysis of Key Brain ROIs and Network Hierarchy 3.3 Analysis of Connectome Skeleton in Brain Networks 4 Conclusion References Vertex Correspondence in Cortical Surface Reconstruction 1 Introduction 2 Methods 3 Experiments and Results 4 Conclusion References Path-Based Heterogeneous Brain Transformer Network for Resting-State Functional Connectivity Analysis 1 Introduction 2 Methodology 2.1 Path-Based Heterogeneous Graph Generation 2.2 HP-GTC: Heterogeneous Path Graph Transformer Convolution Module to Learn Compact Features 2.3 Readout and Prediction 3 Experimental Results 4 Conclusion References Dynamic Graph Neural Representation Based Multi-modal Fusion Model for Cognitive Outcome Prediction in Stroke Cases 1 Introduction 2 Methodology 2.1 Graph Construction and Node Feature Extraction 2.2 Missing Information Compensation Module 2.3 Dynamic Graph Neural Representation 3 Materials and Experiments 3.1 Data and Preparation 3.2 Evaluation Measures 3.3 Experimental Design 4 Results 4.1 Effectiveness of Missing Information Compensation 4.2 Importance of Graph-Based Analysis 4.3 Superiority of Proposed Fusion Model 5 Conclusion and Discussion References Predicting Diverse Functional Connectivity from Structural Connectivity Based on Multi-contexts Discriminator GAN 1 Introduction 2 Method 2.1 Model Overview 2.2 MCGAN 3 Experiments 3.1 Setup 3.2 Results 4 Discussion 5 Conclusion References DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics 1 Introduction 2 Methodology 2.1 Graph Dynamic Mode Decomposition 2.2 Adaptation of Graph-DMD for Nonlinear Graph Dynamics 2.3 Window-Based GraphDMD 3 Experiments 3.1 Dataset 3.2 Baseline Methods 3.3 Simulation Study 3.4 Application of GraphDMD and DeepGraphDMD in HCP Data 4 Results 4.1 Simulation Study 4.2 Application of GraphDMD and DeepGraphDMD in HCP Data 5 Conclusion References Disentangling Site Effects with Cycle-Consistent Adversarial Autoencoder for Multi-site Cortical Data Harmonization 1 Introduction 2 Method 2.1 Vanilla Autoencoder (AE) 2.2 Disentangled Autoencoder (DAE) 2.3 Cycle-Consistent Disentangled Autoencoder (CDAE) 3 Experiments and Results 3.1 Experimental Setting 3.2 Results 4 Conclusion References SurfFlow: A Flow-Based Approach for Rapid and Accurate Cortical Surface Reconstruction from Infant Brain MRI 1 Introduction 2 Methods 2.1 Overview 2.2 Dual-Modal Input 2.3 Loss Function 2.4 Deformation Computation in DMD Modules 2.5 Implementation Details 3 Results 3.1 Data 3.2 Evaluation Metrics 3.3 Results 3.4 Ablation Study 4 Conclusion References Prior-Driven Dynamic Brain Networks for Multi-modal Emotion Recognition 1 Introduction 2 Method 3 Experiment Results 4 Conclusion References Unified Surface and Volumetric Inference on Functional Imaging Data 1 Introduction 2 Methods 3 Results 4 Discussion References TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation 1 Introduction 2 Methods 2.1 Training and Testing Datasets 2.2 TractCloud Framework 2.3 Implementation Details 3 Experiments and Results 3.1 Performance on the Labeled Atlas Dataset 3.2 Performance on the Independently Acquired Testing Datasets 4 Discussion and Conclusion References Robust and Generalisable Segmentation of Subtle Epilepsy-Causing Lesions: A Graph Convolutional Approach 1 Introduction 2 Methods 2.1 Graph Convolutional Network (GCN) for Surface-Based Lesion Segmentation 2.2 Data Augmentation 3 Experiments and Results 3.1 Dataset and Implementation Details 3.2 Results 4 Conclusions and Future Work References Weakly Supervised Cerebellar Cortical Surface Parcellation with Self-Visual Representation Learning 1 Introduction 2 Method 2.1 Cerebellar Surface Reconstruction and Geometric Feature Computation 2.2 Weakly Supervised Cerebellar Patch Representation Learning 2.3 Mapping from Latent Space to Parcellation Labels 3 Experiments 3.1 Dataset and Implementation 3.2 Comparison with the State-of-the-Art Methods 3.3 Different Features’ Influence Analysis 4 Conclusion References Maximum-Entropy Estimation of Joint Relaxation-Diffusion Distribution Using Multi-TE Diffusion MRI 1 Introduction 2 Method 2.1 On the Hausdorff Moment Problem 2.2 Maximum-Entropy Estimation 2.3 Dual Energy Minimization Problems 3 Examples 3.1 Synthetic Data 3.2 Comparison Methods 3.3 In Vivo rdMRI 4 Results 5 Summary References Physics-Informed Conditional Autoencoder Approach for Robust Metabolic CEST MRI at 7T 1 Introduction 2 Methods 3 Results 4 Discussion 5 Conclusion References A Coupled-Mechanisms Modelling Framework for Neurodegeneration 1 Introduction 2 Methodology 2.1 Model Definition 2.2 Bayesian Framework 2.3 Variational Inference 3 Experiments and Results 3.1 Data Processing 3.2 Results 4 Conclusions References A Texture Neural Network to Predict the Abnormal Brachial Plexus from Routine Magnetic Resonance Imaging 1 Introduction 2 Materials and Method 2.1 Dataset Preparation and Preprocessing 2.2 Triple Point Pattern (TPP) 2.3 TPPNet 3 Experiments 3.1 Preparations 3.2 Ablation Studies 3.3 Comparisons 4 Conclusions References Microscopy Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping 1 Introduction 2 Method: Mitosis Detection with Partial Labels 2.1 Labeled Dataset Generation 2.2 Mitosis Detection with Generated Dataset 3 Experiments 4 Conclusion References CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention 1 Introduction 2 Method 2.1 Overview 2.2 Representing Query with Anchor Circle 2.3 Circle Cross Attention 2.4 Circle Regression 2.5 Circle Instance Segmentation 2.6 Generalized Circle IoU 3 Experiment 3.1 Dataset and Evaluation 3.2 Implementation Details 3.3 Main Results 3.4 Ablation Studies 4 Conclusion References A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images 1 Introduction 2 Method 2.1 Hypothesis Tree Construction 2.2 MoTT Network 2.3 Modeling Discrete Optimization Problem 2.4 Track Management 3 Experimental Results 3.1 Quantitative Performance 3.2 Robustness Analysis 4 Conclusion References B-Cos Aligned Transformers Learn Human-Interpretable Features 1 Introduction 2 Related Work 3 Methods 4 Implementation and Evaluation Details 5 Results and Discussion 6 Generalization to Other Architectures 7 Conclusion References PMC-CLIP: Contrastive Language-Image Pre-training Using Biomedical Documents 1 Introduction 2 The PMC-OA Dataset 2.1 Dataset Collection 2.2 Dataset Overview 3 Visual-language Pre-training 4 Experiment Settings 4.1 Pre-training Datasets 4.2 Downstream Tasks 4.3 Implementation Details 5 Result 5.1 PMC-OA surpasses SOTA large-scale biomedical dataset 5.2 PMC-CLIP achieves SOTA across downstream tasks 5.3 Ablation Study 6 Conclusion References Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction 1 Introduction 2 Method 3 Experiments 3.1 Datasets 3.2 Implementation Details: 3.3 Time Arrow Prediction Pretraining 3.4 Downstream Tasks 4 Discussion References Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis 1 Introduction and Related Work 2 Method 2.1 Preliminaries 2.2 Margin Loss with Adaptive Scale (MLAS) 2.3 Open-Space Suppression (OSS) 2.4 Open Margin Cosine Loss (OMCL) 3 Result 3.1 Datasets, Evaluation Metrics, and Implementation Details 3.2 Comparison with State-of-the-Art Methods 3.3 Ablation Studies 4 Conclusion References Exploring Unsupervised Cell Recognition with Prior Self-activation Maps 1 Introduction 2 Method 2.1 Proxy Task 2.2 Prior Self-activation Map 2.3 Downstream Tasks 3 Experiments 3.1 Implementation Details 3.2 Result 4 Conclusion References Prompt-Based Grouping Transformer for Nucleus Detection and Classification 1 Introduction 2 Methodology 2.1 Transformer-Based Centroid Detector 2.2 Grouping Transformer Based Classifier 2.3 Loss Function 2.4 Grouping Prompts Based Tuning 3 Experiments and Results 3.1 Datasets and Implementation Details 3.2 Comparison with the State-of-the-Art 3.3 Ablation Analysis 4 Conclusion References PAS-Net: Rapid Prediction of Antibiotic Susceptibility from Fluorescence Images of Bacterial Cells Using Parallel Dual-Branch Network 1 Introduction 1.1 Method 1.2 Feature Interaction Unit 1.3 Hierarchical Multi-head Self-attention 2 Experiments and Results 2.1 Experimental Setup 2.2 Results 2.3 Robustness to HEp-2 Dataset 3 Conclusion References Diffusion-Based Data Augmentation for Nuclei Image Segmentation 1 Introduction 2 Method 2.1 Unconditional Nuclei Structure Synthesis 2.2 Conditional Histopathology Image Synthesis 3 Experiments and Results 3.1 Implementation Details 3.2 Effectiveness of the Proposed Data Augmentation Method 4 Conclusion References Unsupervised Learning for Feature Extraction and Temporal Alignment of 3D+t Point Clouds of Zebrafish Embryos 1 Introduction 2 Methods 2.1 Autoencoder 2.2 Regression Network 3 Experimental Results and Discussions 3.1 Data Sets and Evaluation 3.2 Experimental Settings 3.3 Experimental Results 4 Conclusion References 3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers 1 Introduction 2 Related Work 3 Method 3.1 Baseline Framework 3.2 Spatio-Temporal Transformer Res-UNET (STT-UNET) 4 Experiments 4.1 Results 5 Conclusion References Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning 1 Introduction 2 Method 2.1 Visual Prompt Tuning 2.2 Optimization 3 Experiments and Discussion 3.1 Datasets 3.2 Implementation Details 3.3 Results 4 Conclusion References Pick and Trace: Instance Segmentation for Filamentous Objects with a Recurrent Neural Network 1 Introduction 2 Method 2.1 Pick: Tip Points Detection Module 2.2 Trace: A Recurrent Network for Filament Tracing 3 Experiments 3.1 Synthetic Dataset 3.2 Microtubule Dataset 3.3 P. Rubescens Dataset 3.4 C. Elegans Dataset 4 Conclusion References BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior 1 Introduction 2 Methods 2.1 Revisiting Dual-View LSFM Fusion Using Bayes 2.2 Image Clarity Characterization with Image Formation Prior 2.3 Least Squares Smoothness of Focus-Defocus Boundary 2.4 Focus-Defocus Boundary Inference via EM 2.5 Competitive Methods 3 Results and Discussion 3.1 Evaluation on LSFM Images with Synthetic Blur 3.2 Evaluation on LSFM Images with Real Blur 4 Conclusion References LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network 1 Introduction 2 Method 2.1 Correction Module and Bottleneck 2.2 Update Module 2.3 Loss Function 3 Experiments 3.1 Setup 3.2 Results 4 Conclusion References Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set 1 Introduction 2 Methods 2.1 Variational Deep Embedding (VaDE) 2.2 Conditionally Decoded Variational Deep Embedding (CDVaDE) 2.3 Deep Embedding Clustering (DEC) 2.4 Related Works in Medical Imaging 3 Experiments 3.1 Colored MNIST 3.2 Application to a Digital Pathology Dataset 4 Conclusion References Weakly-Supervised Drug Efficiency Estimation with Confidence Score: Application to COVID-19 Drug Discovery 1 Introduction 2 Dataset 3 Methods 3.1 Image Preprocessing and Embedding 3.2 Data Augmentation with Weak Labels 3.3 Confident Hit Predictor 4 Experiments and Results 4.1 Experimental Settings 4.2 Representation Quality Assurance 4.3 Disease Scores Quality Assurance 4.4 Confidence Scores Quality Assurance 4.5 Evaluation 5 Conclusion References Author Index