Computer vision Books

Book Synopsis.- Single-source Domain Generalization for Coronary Vessels Segmentation in X-ray Angiography. .- Constraint-Based Model in Multimodal Learning to Improve Ventricular Arrhythmia Prediction. .- Automated estimation of cardiac stroke volumes from computed tomography. .- Peridevice leaks following left atrial appendage occlusion - analysis with morphology descriptive centerlines and explainable graph attention network. .- Improved 3D Whole Heart Geometry from Sparse CMR Slices. .- CavityBASNet: Cavity-focused Biatrial Automatic Segmentation on LGE MRI with augmented input channel and left-right myocardium splitting. .- A novel MRI-based electrophysiological computational model of progressive doxorubicin-induced fibrosis in the left ventricle. .- Quantitative comparison of blood flow patterns from in silico simulations and 4D flow data before and after left atrial occlusion. .- Panoramic anatomical context in 3D intracardiac echocardiography (ICE) with 3D registration and geometry-based image fusion. .- Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions. .- Beyond the standards: Fully-Automated Aortic Annulus Segmentation on Contrast-free Magnetic Resonance Imaging using a Computational Aorta Unwrapping Method. .- Coronary Artery Calcium Scoring from Non-Contrast Cardiac CT Using Deep Learning With External Validation. .- Effective approach based on student-teacher self-supervised deep learning for Multi-class Bi-Atrial Segmentation Challenge. .- Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model. .- HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss. .- A Multi-Contrast Cardiac MRI Reconstruction Method Using an Advanced Unrolled Network Architecture. .- Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle. .- Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxil iary Refinement Network. .- Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients. .- Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs. .- An Ensemble of 3D Residual Encoder UNet Models for Solving Multi-Class Bi-Atrial Segmentation Challenge. .- Evaluating Convolution, Attention, and Mamba Based U-Net Models for Multi-Class Bi-Atrial Segmentation from LGE-MRI. .- On the Foundation Model for Cardiac MRI Reconstruction. .- Multi-Loss 3D Segmentation for Enhanced Bi-Atrial Segmentation. .- Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features. .- Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI. .- Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation. .- A self-distillation bi-atrial segmentation network for Cardiac MRI. .- Adaptive Unrolling Applied to the CMRxRecon2024 Callenge. .- Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space. .- Rotor Core Projection Ablation (RCPA): Novel Computational Approach to Catheter Ablation Therapy for Atrial Fibrillation. .- Automated pipeline for regional epicardial adipose tissue distribution analysis in the left atrium. .- Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging. .- SBAW-Net: Segmentation of Bi-Atria and Wall Network - Offering Valuable Insights into Challenge Data. .- ResNet-based Convolutional Framework for Segmenting Left Atrial Scars and Cavities. .- EAT-Mamba: Epicardial Adipose Tissue Segmentation from Multi-modal Dixon MRI. .- Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging. .- Exploring CNN and Transformer Architectures for Multi-class Bi-Atrial Segmentation from Late Gadolinium-Enhanced MRI. .- EigenBoundaries for the temporally regularized segmentation of echocardiographic images. .- Dynamic Cardiac MRI Reconstruction via Separate Optimization of K-space and Hybrid-domian Spatial-temporal Feature Fusion. .- an Interpretable Learning of Risk Explain Ventricular Arrhythmia Mechanism. .- 3D Left Ventricular Reconstruction from 2D Echocardiograms for Reliable Volume Estimation. .- Comparing Left Atrial Spontaneous Echo Contrast Intensity with Gaussian Process Emulator Predictions. .- UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction. .- An All-in-one Approach for Accelerated Cardiac MRI Reconstruction. .- Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation.

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Book SynopsisIntroduction.- Background.- Research objectives and method.- Datasets.- Comparison of the reviewed methods.- Conclusion.

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Book SynopsisPart 1: Pre-training and Datasets.- Chapter 1: LAION-5B: A Massive Open Image-Text Dataset.- Chapter 2: Efficient Training of Large-Scale Vision-Language Models.- Chapter 3: Scaling Laws for Contrastive Language-Image Learning.- Chapter 4: Scaling Up Vision-Language Models for Generic Tasks.- Chapter 5: Searching for Next-Gen Multimodal Datasets.- Part 2: Prompting and Generalization.- Chapter 6: Soft Prompt Learning for Vision-Language Models.- Chapter 7: Unified Prompting for Vision and Language.- Chapter 8: Zero-Shot Image Classification with Custom Prompts.- Chapter 9: Enhancing Vision-Language Models with Feature Adapters.- Chapter 10: Automatic Optimization of Prompting Architectures.- Chapter 11: Open-Vocabulary Calibration for VL Models.- Part 3: Applications.- Chapter 12: Open-Vocabulary DETR with Conditional Matching.- Chapter 13: Extracting Dense Labels from CLIP.- Chapter 14: PointCLIP: Understanding Point Clouds with VL.- Chapter 15: Diffusion-Based Relation Inversion from Images.- Chapter 16: Text-to-Video Generation.- Chapter 17: Text-Driven Human Motion Generation.- Chapter 18: Zero-Shot Text-Driven 3D Avatar Generation.- Chapter 19: Zero-Shot Text-Driven HDR Panorama Generation.

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Book SynopsisComputer-aided diagnosis/surgery: Concepts from Neurons: Building Interpretable Medical Image Diagnostic Models by Dissecting Opaque Neural Networks.- BioSonix: Can Physics-based Sonification Perceptualize Tissue Deformations from Tool Interactions? Brain: Explainable Deep Model for Understanding Neuropathological Events Through Neural Symbolic Regression.- A Multi-Layer Neural Transport Model for Characterizing Pathology Propagation in Neurodegenerative Diseases.- Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.- Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction.- Disentangle disease-relevant patterns from irrelevant patterns in fMRI analysis using equivariant and contrastive learning. Diffusion models: Continuous Diffusion Model for Self-supervised Denoising and Super-resolution on Fluorescence Microscopy Images.- Self-Supervised Denoising of Diffusion MRI Data with Efficient Collaborative Diffusion Model.- MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection. Self-supervised learning: Taming Masked Image Modeling for Chest X-ray Diagnosis by Incorporating Clinical Visual Priors.- Diffusion MAE: Paving the Way for Representation Learning of Diffusion MRI.- Resolving quantitative MRI model degeneracy in self-supervised machine learning. Vision-language models: Knowledge-enhanced Hyperbolic Language-Image Pretraining for Zero-shot Learning.- Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation.- Hierarchical CLIPs for Fine-grained Anatomical Lesion Localization from Whole-body PET/CT Images.- Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.- Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models.- Full Conformal Adaptation of Medical Vision-Language Models.- A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text? Shape analysis: ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings.- LEDA: Log-Euclidean Diffeomorphism Autoencoder for Efficient Statistical Analysis of Diffeomorphisms.- CoRLD: Contrastive Representation Learning of Deformable Shapes in Images. Time-series image analysis: 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images.- Brightness-Invariant Tracking Estimation in Tagged MRI.- SafeTriage: Facial Video De-identification for Privacy-Preserving Stroke Triage.

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Book SynopsisClassification/Detection: SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection.- Hierarchical Neural Cellular Automata for Lightweight Microscopy Image Classification.- PathTTT: Test-Time Training with Meta-Auxiliary Learning for Pathology Image Classification. Registration: Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative.- GSSD: A Self-Distillation Paradigm with Gradient Surgery for End-to-End Deformable Image Registration.- Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness.- Vascular-topology-aware Deep Structure Matching for 2D DSA and 3D CTA Rigid Registration.- Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing. Reconstruction: Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching.- Optimization of acquisition schemes towards a better estimation of microstructure parameters in multidimensional diffusion MRI.- Bilinear Projector: Mitigating Discretization Artifacts in Model Based Iterative Reconstruction for X-ray CT.- Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging. Image synthesis: 3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from Cortical Surfaces.- Cascaded Diffusion Model and Segment Anything Model for Medical Image Synthesis via Uncertainty-Guided Prompt Generation.- DIReCT: Domain-Informed Rectified Flow for Controllable Brain MRI to PET Translation.- IGG: Image Generation Informed by Geodesic Dynamics in Deformation Spaces. Image enhancement: Cycle-consistent zero-shot through-plane super-resolution for anisotropic head MRI.- Bayesian Learning with Stochastic Perturbations and Langevin Expectation Maximization for Unsupervised DNN Image Quality Enhancement. Segmentation: MC-NuSeg: Multi-Contour Aware Nuclei Instance Segmentation with Segment Anything Model.- Pitfalls of topology-aware image segmentation.- GeoT: Geometry-guided Instance-dependent  Transition Matrix for Semi-supervised Tooth Point Cloud Segmentation.- RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation.- Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning.- SkeIite: Compact Neural Networks for Efficient Iterative Skeletonization.- VerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation.

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Book Synopsis.- Frontiers in Computational Pathology..- Transductive Survival Ranking for Pan-cancer Automatic Risk Stratification using Whole Slide Images..- Benchmarking Histopathology Foundation Models in a Multi-center Dataset for Skin Cancer Subtyping..- MitoNet: Efficient Ki-67 Detection in H&E-Stained Images..- ASTER: Automated Segmentation of Endometrial Histology Images for Reproductive Health Assessment..- Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images..- SMatt-DINO: Spatially Aware Masked Attention Network for High Resolution Brain Image Classification..- Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets..- SWIFT-Reg: Slide-Wide Intelligent Feature-based Tissue Registration..- Learnable Moran’s Index for Modeling Spatial Autocorrelation in Whole Slide Images to Predict Breast Cancer Outcomes..- Image Synthesis and Generative Artificial Intelligence..- Augmenting Chest X-ray Datasets with Non-Expert Annotations..- Leveraging Synthetic Data for Whole-Body Segmentation in X-ray Images..- Transform(AI)ng Radiology with CheXSBT: Integrating Dual-Attention Swin Transformer with BERT for Seamless Chest X-Ray Report Generation..- Cardiac Ultrasound Video Generation Using a Diffusion Model with Temporal Transformer..- KCLVA: Knowledge-enhanced Contrastive Learning and View-specific Attention for Chest X-ray Report Generation..- BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization..- MediAug: Exploring Visual Augmentation in Medical Imaging..- On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?..- DiNO-Diffusion: Scaling Medical Diffusion Models via Self-Supervised Pre-Training..- Knowledge-Driven Hypothesis Generation for Burn Diagnosis from Ultrasound with Vision-Language Model..- Multimodal Federated Learning With Missing Modalities through Feature Imputation Network..- Parameter-Efficient Multimodal Adaptation for Certified Robustness of Medical Vision-Language Models.

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Book Synopsis.- Image-guided Diagnosis..- FD-SSD: Semi-Supervised Detection of Bone Fenestration and Dehiscence in Intraoral Images..- Interpretable Prediction of Lymph Node Metastasis in Rectal Cancer MRI Using Variational Autoencoders..- Self-Guided SwinTransformer Improves Breast Cancer Detection Through Iterative Attention-Based Zooming..- Can AI Be Faster, Accurate, and Explainable? SpikeNet Makes It Happen..- A Novel Feature-Prioritized Loss Function for Enhanced Pneumonia Segmentation in Chest X-rays..- Bridging Accuracy and Explainability: A SHAP-Enhanced CNN for Skin Cancer Diagnosis..- Multi-Scale WSI Analysis: A Cascade Framework for Efficient Breast Cancer Metastasis Detection..- Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction..- Modified CBAM: Sub-Block Pooling for Improved Channel and Spatial Attention..- WSI-AL: A Novel Active Learning Framework for Whole Slide Image Selection..- A Deep-learning Approach for Diagnosing and Grading Ankylosing Spondylitis Sacroiliitis by X-ray Images..- Towards Breast Tumor Aggressiveness Classification in Digital Mammograms Using Boundary-Aware Segmentation and Feature Analysis..- Image-guided Intervention..- Joint Dento-Facial Shape Model..- Out-of-Distribution Detection in Gastrointestinal Vision by Estimating Nearest Centroid Distance Deficit..- Deep Learning-Driven Pipeline for Automated Wound Measurement of Chronic Wounds..- Midline-constrained Loss in the Anatomical Landmark Segmentation of 3D Liver Models..- DepthClassNet: A Multitask Framework for Monocular Depth Estimation and Texture Classification in Endoscopic Imaging..- Assessing the Generalization Performance of SAM for Ureteroscopy Scene Segmentation and Understanding..- Modelling Uncertainty in Graph Convolutional Networks for Edge Detection in Mammograms..- Classification of Gastroscopy Images Under extreme Class Imbalance: A Deep Learning Pipeline..- Temporally Consistent Smoke Removal from Endoscopic Video Images..- Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions..- EfficientDet with Knowledge Distillation and Instance Whitening for Real-time and Generalisable Polyp Detection.

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Book Synopsis.- Medical Image Segmentation..- TransE2UNet: Edge Guided TransEfficientUNET for Generalized Colon Polyp Segmentation from Endoscopy Images..- CA-Seg: An Attribute-based Medical Image Segmentation Framework for Unified Out-of-distributed Medical Image Segmentation..- TotalSegmentator 2D: A Tool for Rapid Anatomical Structure Analysis..- Promptable Cancer Segmentation Using Minimal Expert-curated Data..- SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours..- Semantic Segmentation with Spreading Scribbles..- A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology..- Exploring Context-Switching in Medical Image Retrieval Using Segmentation Models..- Segmentation in Histopathology Utilising Simulated Masked Patches..- A Feature-Driven Acquisition Strategy Using Scale-Invariant Descriptors for Deep Active Learning in Preclinical CT Segmentation..- Quantifying Inter-Annotator Agreement and Generalist Model Limitations in Imaging Mass Cytometry Single Cell Segmentation..- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer..- DRASU-Net: Dual-backbone and Residual Atrous Squeeze module-aided U-Net Model for Polyp Segmentation..- PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation..- Intraoperative Segmentation Through Deep Learning and Mask Post-processing in Laparoscopic Liver Surgery..- Retinal and Vascular Image Analysis..- Hessian-based Deep Retinal Vessel Segmentation with Extremely Few Annotations..- Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis..- Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage..- Enhanced Coronary Artery Segmentation in CTCA Using Bridging Centreline Integration..- QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation..- Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis..- GenVOG: A Diffusion Probabilistic Framework for Patient-Independent Pose-Guided Nystagmus Video-Oculography (VOG) Generation..- Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans.

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Book Synopsis.- MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis..- GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-EnhancedUNet for Synthetic CT Generation from CBCT..- 2D to 3D MR Image Super-Resolution using Cross-Contrast Guidance..- 3D Super-Resolution for Enhancing Compression Fracture Detection in Thick Slice CT: Diffusion Models vs GANs..- From Tissue-Mimicking Phantoms to Physics-Based Scans: Synthetic OCT forFew-Shot Foundation Model Training..- Multi-modal Brain MRI Synthesis with nnU-Net: Exploring Segmentation Per formance and Cross-Modality Relationships..- Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning..- From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collima tors via Hough Transform..- VIOLET: A framework for combined Volumetric Image registration via Opti mization and Learning for Efficient image Translation..- Generation of Controllable and Photorealistic Synthetic Cataract Surgery Im ages: Blending 3D Models and Real-World Data..- Unsupervised MRI Harmonization via Parameter Prediction and Super-ResolvedMPMs..- Learning Mechanistic Subtypes of Neurodegeneration with a Physics-InformedVariational Autoencoder Mixture Model..- FastDTI: A 3D Scale-arbitrary Super-resolution Autoencoder Residual DenseNetwork for DTI..- Lesion-Aware CT-to-MRI Synthesis using a Mask-Informed Diffusion with Adaptive Weighted Loss (MIDAS)..-Conditional Iterative α-(de)Blending Model for CBCT-tosCT Synthesis: Towards a Deterministic and Simple Process..- Synthesizing Accurate and Realistic T1-weighted Contrast-Enhanced MR Images using Posterior-Mean Rectified Flow..- Clustering-based Stain Augmentation: Templates for Periodic Acid-Schiff BiopsyImages.

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Book Synopsis.- LTCXNet: Tackling Long-Tailed Multi-Label Classification and RacialBias in Chest X-Ray Analysis..- Fairness and Robustness of CLIP-Based Models for Chest X-rays..- ShortCXR: Benchmarking Self-Supervised Learning Methods forShortcut Mitigation in Chest X-Ray Interpretation..- How Fair Are Foundation Models? Exploring the Role of CovariateBias in Histopathology..- The Cervix in Context: Bias Assessment in Preterm Birth Prediction..- Identifying Gender-Specific Visual Bias Signals in Skin Lesion Classification..- Fairness-Aware Data Augmentation for Cardiac MRI usingText-Conditioned Diffusion Models..- Exploring the interplay of label bias with subgroup size and separability:A case study in mammographic density classification..- Does a Rising Tide Lift All Boats? Bias Mitigation for AI-based CMRSegmentation..- MIMM-X: Disentangeling Spurious Correlations for Medical ImageAnalysis..- Predicting Patient Self-reported Race From Skin Histological Imageswith Deep Learning..- Robustness and sex differences in skin cancer detection: logisticregression vs CNNs..- Sex-based Bias Inherent in the Dice Similarity Coefficient: A ModelIndependent Analysis for Multiple Anatomical Structures..- The Impact of Skin Tone Label Granularity on the Performance andFairness of AI Based Dermatology Image Classification Models..- Causal Representation Learning with Observational Grouping for CXRClassification..- Invisible Attributes, Visible Biases: Exploring Demographic Shortcutsin MRI-based Alzheimer’s Disease Classification..- Fair Dermatological Disease Diagnosis through Auto-weightedFederated Learning and Performance-aware Personalization..- Assessing Annotator and Clinician Biases in an Open-Source-BasedTool Used to Generate Head CT Segmentations for Deep LearningTraining..- meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis..- Revisiting the Evaluation Bias Introduced by Frame SamplingStrategies in Surgical Video Segmentation Using SAM2..- Disentanglement and Assessment of Shortcuts in OphthalmologicalRetinal Imaging Exams.

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Book Synopsis

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Book SynopsisChapter 1. Background Introduction.- Chapter 2. Datasets.- Chapter 3. Algorithms.- Chapter 4. Applications.- Chapter 5. Conclusion and Future Directions.

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