Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search -- Sttructured (De)composable Representations Trained with Neural Networks -- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling -- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins -- Abstract Echo State Networks -- Minimal Complexity Support Vector Machines -- Named Entity Disambiguation at Scale -- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds -- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database -- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario -- A deep learning approach for efficient registration of dual view mammography -- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology -- Applications of Generative Adversarial Networks to Dermatologic Imaging -- Typing Plasmids with Distributed Sequence Representation -- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes -- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools -- A Hybrid Deep Learning Approach For Forecasting Air Temperature -- Using CNNs to optimize numerical simulations in geotechnical engineering -- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification -- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture -- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis -- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.

This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.