Search Results for author: Sotirios A. Tsaftaris

Found 70 papers, 37 papers with code

MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection

no code implementations29 May 2024 Raman Dutt, Pedro Sanchez, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy Hospedales

We perform our experiments for the specific task of medical image generation and outperform existing state-of-the-art training-time mitigation strategies by fine-tuning as few as 0. 019% of model parameters.

Image Generation Medical Image Generation +1

Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models

no code implementations19 Apr 2024 Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris

In this work, we use a publicly available Foundation Model, namely the Latent Diffusion Model, to solve this challenging task.

Contrastive Learning Phrase Grounding

Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification

no code implementations26 Mar 2024 Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio

Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data.

Few-Shot Learning Image Classification +2

Inference Stage Denoising for Undersampled MRI Reconstruction

1 code implementation12 Feb 2024 Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris

In this work, by employing a conditional hyperparameter network, we eliminate the need of augmentation, yet maintain robust performance under various levels of Gaussian noise.

Data Augmentation Denoising +1

Group Distributionally Robust Knowledge Distillation

no code implementations1 Nov 2023 Konstantinos Vilouras, Xiao Liu, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris

Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one.

Knowledge Distillation

Compositional Representation Learning for Brain Tumour Segmentation

no code implementations10 Oct 2023 Xiao Liu, Antanas Kascenas, Hannah Watson, Sotirios A. Tsaftaris, Alison Q. O'Neil

For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations.

Representation Learning

Adapting Vision Foundation Models for Plant Phenotyping

no code implementations ICCV 2023 Feng Chen, Mario Valerio Giuffrida, Sotirios A. Tsaftaris

The experimental results show that a foundation model can be efficiently adapted to multiple plant phenotyping tasks, yielding similar performance as the state-of-the-art (SoTA) models specifically designed or trained for each task.

Decoder Instance Segmentation +2

Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction

1 code implementation25 Sep 2023 Yuning Du, Yuyang Xue, Rohan Dharmakumar, Sotirios A. Tsaftaris

Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time.

Fairness MRI Reconstruction

Generative AI for Medical Imaging: extending the MONAI Framework

2 code implementations27 Jul 2023 Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.

Anomaly Detection Denoising +2

A Causal Ordering Prior for Unsupervised Representation Learning

no code implementations11 Jul 2023 Avinash Kori, Pedro Sanchez, Konstantinos Vilouras, Ben Glocker, Sotirios A. Tsaftaris

Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables.

Causal Discovery counterfactual +2

Compositionally Equivariant Representation Learning

no code implementations13 Jun 2023 Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris

By modelling the compositional representations with learnable von-Mises-Fisher (vMF) kernels, we explore how different design and learning biases can be used to enforce the representations to be more compositionally equivariant under un-, weakly-, and semi-supervised settings.

Anatomy Image Segmentation +3

Understanding metric-related pitfalls in image analysis validation

no code implementations3 Feb 2023 Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.

The role of noise in denoising models for anomaly detection in medical images

1 code implementation19 Jan 2023 Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil

Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.

Denoising Unsupervised Anomaly Detection

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

no code implementations15 Nov 2022 Anjun Hu, Jean-Pierre R. Falet, Brennan S. Nichyporuk, Changjian Shui, Douglas L. Arnold, Sotirios A. Tsaftaris, Tal Arbel

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e. g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs.

Anatomy Disentanglement +1

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

no code implementations31 Oct 2022 Brennan Nichyporuk, Jillian Cardinell, Justin Szeto, Raghav Mehta, Jean-Pierre R. Falet, Douglas L. Arnold, Sotirios A. Tsaftaris, Tal Arbel

This is particularly important in the context of medical image segmentation of pathological structures (e. g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others.

Attribute Image Segmentation +2

Diffusion Models for Causal Discovery via Topological Ordering

1 code implementation12 Oct 2022 Pedro Sanchez, Xiao Liu, Alison Q O'Neil, Sotirios A. Tsaftaris

We introduce theory for updating the learned Hessian without re-training the neural network, and we show that computing with a subset of samples gives an accurate approximation of the ordering, which allows scaling to datasets with more samples and variables.

Causal Discovery

vMFNet: Compositionality Meets Domain-generalised Segmentation

1 code implementation29 Jun 2022 Xiao Liu, Spyridon Thermos, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris

Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image.

Anatomy Image Segmentation +3

Why patient data cannot be easily forgotten?

no code implementations29 Jun 2022 Ruolin Su, Xiao Liu, Sotirios A. Tsaftaris

With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient's data within AI models.

Metrics reloaded: Recommendations for image analysis validation

1 code implementation3 Jun 2022 Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, A. Emre Kavur, Carole H. Sudre, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger

The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.

Instance Segmentation object-detection +2

Unintended memorisation of unique features in neural networks

no code implementations20 May 2022 John Hartley, Sotirios A. Tsaftaris

Neural networks pose a privacy risk due to their propensity to memorise and leak training data.

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

no code implementations15 Mar 2022 Tian Xia, Pedro Sanchez, Chen Qin, Sotirios A. Tsaftaris

To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task.

Classification counterfactual +1

Diffusion Causal Models for Counterfactual Estimation

1 code implementation21 Feb 2022 Pedro Sanchez, Sotirios A. Tsaftaris

We consider the task of counterfactual estimation from observational imaging data given a known causal structure.

counterfactual

Measuring Unintended Memorisation of Unique Private Features in Neural Networks

no code implementations16 Feb 2022 John Hartley, Sotirios A. Tsaftaris

Neural networks pose a privacy risk to training data due to their propensity to memorise and leak information.

Image Classification

Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers

1 code implementation12 Feb 2022 Grzegorz Jacenków, Alison Q. O'Neil, Sotirios A. Tsaftaris

We use the indication field to drive better image classification, by taking a transformer network which is unimodally pre-trained on text (BERT) and fine-tuning it for multimodal classification of a dual image-text input.

Classification Image Classification

FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection

no code implementations30 Nov 2021 Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris

By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots.

One-Class Classification Out-of-Distribution Detection +2

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary

1 code implementation28 Oct 2021 Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris

OMASGAN addresses the rarity of anomalies by generating strong and adversarial OoD samples on the distribution boundary using only normal class data, effectively addressing mode collapse.

Anomaly Detection Data Augmentation +2

FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection

no code implementations29 Sep 2021 Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris

We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models, including classification.

One-Class Classification Out-of-Distribution Detection +2

Learning Disentangled Representations in the Imaging Domain

1 code implementation26 Aug 2021 Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris

Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.

Representation Learning

Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation

no code implementations24 Jul 2021 Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris

In this paper, we create a GAN-based tail formation model for anomaly detection, the Tail of distribution GAN (TailGAN), to generate samples on the tail of the data distribution and detect anomalies near the support boundary.

Generative Adversarial Network Unsupervised Anomaly Detection

Controllable cardiac synthesis via disentangled anatomy arithmetic

1 code implementation4 Jul 2021 Spyridon Thermos, Xiao Liu, Alison O'Neil, Sotirios A. Tsaftaris

Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e. g. MRI), plausible new cardiac images are created with the target characteristics.

Anatomy

Survey: Leakage and Privacy at Inference Time

no code implementations4 Jul 2021 Marija Jegorova, Chaitanya Kaul, Charlie Mayor, Alison Q. O'Neil, Alexander Weir, Roderick Murray-Smith, Sotirios A. Tsaftaris

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data.

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

Semi-supervised Pathology Segmentation with Disentangled Representations

1 code implementation5 Sep 2020 Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang, Giorgos Papanastasiou, Scott Semple, Mark Dweck, David Semple, Rohan Dharmakumar, Sotirios A. Tsaftaris

The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.

Anatomy Disentanglement +1

Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling

1 code implementation5 Sep 2020 Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias, Sotirios A. Tsaftaris

Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections.

Management Segmentation

Measuring the Biases and Effectiveness of Content-Style Disentanglement

4 code implementations27 Aug 2020 Xiao Liu, Spyridon Thermos, Gabriele Valvano, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris

In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.

Disentanglement Image-to-Image Translation

Disentangled Representations for Domain-generalized Cardiac Segmentation

1 code implementation26 Aug 2020 Xiao Liu, Spyridon Thermos, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris

Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains.

Anatomy Cardiac Segmentation +5

INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs

1 code implementation21 Aug 2020 Grzegorz Jacenków, Alison Q. O'Neil, Brian Mohr, Sotirios A. Tsaftaris

We evaluate the method on two datasets: a new CLEVR-Seg dataset where we segment objects based on location, and the ACDC dataset conditioned on cardiac phase and slice location within the volume.

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

3 code implementations2 Jul 2020 Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris

We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.

Deep Attention Image Segmentation +6

Have you forgotten? A method to assess if machine learning models have forgotten data

no code implementations21 Apr 2020 Xiao Liu, Sotirios A. Tsaftaris

In the era of deep learning, aggregation of data from several sources is a common approach to ensuring data diversity.

BIG-bench Machine Learning

Pseudo-healthy synthesis with pathology disentanglement and adversarial learning

1 code implementation20 Apr 2020 Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris

In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy.

Anomaly Detection Disentanglement

Learning to synthesise the ageing brain without longitudinal data

1 code implementation4 Dec 2019 Tian Xia, Agisilaos Chartsias, Chengjia Wang, Sotirios A. Tsaftaris

Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable).

Anatomy

Temporal Consistency Objectives Regularize the Learning of Disentangled Representations

1 code implementation29 Aug 2019 Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris

There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and costly).

Anatomy Disentanglement

Disentangled Representation Learning in Cardiac Image Analysis

4 code implementations22 Mar 2019 Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris

We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics.

Anatomy Computed Tomography (CT) +3

The Generalized Complex Kernel Least-Mean-Square Algorithm

no code implementations22 Feb 2019 Rafael Boloix-Tortosa, Juan José Murillo-Fuentes, Sotirios A. Tsaftaris

Also, the flexibility of the proposed generalized approach is tested in a second experiment with non-independent real and imaginary parts.

regression

Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization

no code implementations10 Jan 2019 Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris

Pseudo healthy synthesis, i. e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation.

Anomaly Detection Data Augmentation +2

Factorised spatial representation learning: application in semi-supervised myocardial segmentation

1 code implementation19 Mar 2018 Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Scott Semple, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris

Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI.

Medical Image Segmentation Myocardium Segmentation +1

Leveraging multiple datasets for deep leaf counting

no code implementations5 Sep 2017 Andrei Dobrescu, Mario Valerio Giuffrida, Sotirios A. Tsaftaris

While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation).

Instance Segmentation Segmentation +1

Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations

no code implementations28 Jun 2016 Mario Valerio Giuffrida, Sotirios A. Tsaftaris

In this paper, we propose the Theta-Restricted Boltzmann Machine ({\theta}-RBM in short), which builds upon the original RBM formulation and injects the notion of rotation-invariance during the learning procedure.

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