Intro; Additional Workshop Editors; OR 2.0 2019 Preface; OR 2.0 2019 Organization; MLCN 2019 Preface; MLCN 2019 Organization; Contents; Proceedings of the 2nd International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019); Feature Aggregation Decoder for Segmenting Laparoscopic Scenes; 1 Introduction; 2 Method; 2.1 Xception Encoder; 2.2 Feature Aggregation Decoder; 3 Experimental Results and Discussions; 4 Conclusions; References; Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories; 1 Introduction; 2 Materials and Methods

3 Experiments4 Results; 5 Discussion and Conclusion; References; Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke; 1 Introduction; 2 Method; 2.1 Guided-Unsharp Upsample Loss; 2.2 Aggregate Loss Function; 2.3 Atrous Convolution Feature Extraction Module; 2.4 Generator and Discriminator Networks; 3 Experimentation and Results; 3.1 Dataset and Implementation Details; 3.2 Results; 4 Conclusion; References; Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration; 1 Introduction; 2 Methods; 2.1 RSD Model

2.2 Unsupervised Temporal Video Segmentation Model2.3 Combined Learning Pipelines; 2.4 Corridor-Based RSD Loss Function; 3 Evaluation; 3.1 Baselines; 3.2 Results; 4 Conclusion; References; Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis; 1 Introduction; 2 Methods; 2.1 Multispectral Imaging Hardware; 2.2 End-to-End Deep Learning Pipeline for Multispectral Image Analysis; 3 Experiments and Results; 3.1 In Silico Quantitative Validation; 3.2 In Vivo Qualitative Validation; 4 Discussion; References; Towards a Cyber-Physical Systems Based Operating Room of the Future

1 Introduction2 Methods; 2.1 Intelligent Surgical Theatre Architecture; 2.2 Cyber-Twin; 2.3 Cognitive Engine and Machine Learning; 3 RadioFrequency Ablation Needle Insertion Robot; 4 Discussion and Conclusion; References; Proceedings of the 2nd International Workshop on Machine Learning in Clinical Neuroimaging: Entering the Era of Big Data via Transfer Learning and Data Harmonization (MLCN 2019); Deep Transfer Learning for Whole-Brain FMRI Analyses; 1 Introduction; 2 Methods; 2.1 Data; 2.2 DeepLight; 3 Results; 3.1 Pre-training Data; 3.2 Test Data; 4 Conclusion; References

Knowledge Distillation for Semi-supervised Domain Adaptation1 Introduction; 2 Related Work; 3 Methods; 3.1 Knowledge Distillation for Domain Adaptation; 4 Experiments and Results; 4.1 Databases; 4.2 Experimental Setup; 4.3 Results; 5 Discussion; References; Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors; 1 Introduction; 2 Data; 2.1 Data Pre-processing and Feature Extraction; 3 The Relevance Vector Machine for Data Harmonization; 4 Results; 4.1 Verification of Observable Correlations in Data; 4.2 Harmonization of Healthy Population Data Based on RVM