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1. Data augmentation for deep learning based accelerated MRI reconstruction with limited data

   Fabian, Zalan; Heckel, Reinhard; Soltanolkotabi, Mahdi (July 2021, International Conference on Machine Learning)

   Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that image. To achieve state-of-the-art performance, training on large and diverse sets of images is considered critical. However, it is often difficult and/or expensive to collect large amounts of training images. Inspired by the success of Data Augmentation (DA) for classification problems, in this paper, we propose a pipeline for data augmentation for accelerated MRI reconstruction and study its effectiveness at reducing the required training data in a variety of settings. Our DA pipeline, MRAugment, is specifically designed to utilize the invariances present in medical imaging measurements as naive DA strategies that neglect the physics of the problem fail. Through extensive studies on multiple datasets we demonstrate that in the low-data regime DA prevents overfitting and can match or even surpass the state of the art while using significantly fewer training data, whereas in the high-data regime it has diminishing returns. Furthermore, our findings show that DA can improve the robustness of the model against various shifts in the test distribution. 

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2. Data augmentation for deep learning based accelerated MRI reconstruction with limited data

   Fabian, Zalan; Heckel, Reinhard; Soltanolkotabi, Mahdi (July 2021, Proceedings of the 38th International Conference on Machine Learning)

   null (Ed.)

   Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that image. To achieve state-of-the-art performance, training on large and diverse sets of images is considered critical. However, it is often difficult and/or expensive to collect large amounts of training images. Inspired by the success of Data Augmentation (DA) for classification problems, in this paper, we propose a pipeline for data augmentation for accelerated MRI reconstruction and study its effectiveness at reducing the required training data in a variety of settings. Our DA pipeline, MRAugment, is specifically designed to utilize the invariances present in medical imaging measurements as naive DA strategies that neglect the physics of the problem fail. Through extensive studies on multiple datasets we demonstrate that in the low-data regime DA prevents overfitting and can match or even surpass the state of the art while using significantly fewer training data, whereas in the high-data regime it has diminishing returns. Furthermore, our findings show that DA improves the robustness of the model against various shifts in the test distribution. 

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3. Consciousness is supported by near-critical slow cortical electrodynamics

   https://doi.org/10.1073/pnas.2024455119  

   Toker, Daniel; Pappas, Ioannis; Lendner, Janna D.; Frohlich, Joel; Mateos, Diego M.; Muthukumaraswamy, Suresh; Carhart-Harris, Robin; Paff, Michelle; Vespa, Paul M.; Monti, Martin M.; et al (February 2022, Proceedings of the National Academy of Sciences)

   Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting. 

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4. Computational and experimental modulation of a noisy chaotic neuronal system

   https://doi.org/10.1063/5.0130874  

   Gonzalez, Josselyn; Follmann, Rosangela; Rosa, Epaminondas; Stein, Wolfgang (March 2023, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   In this work, we study the interplay between chaos and noise in neuronal state transitions involving period doubling cascades. Our approach involves the implementation of a neuronal mathematical model under the action of neuromodulatory input, with and without noise, as well as equivalent experimental work on a biological neuron in the stomatogastric ganglion of the crab Cancer borealis. Our simulations show typical transitions between tonic and bursting regimes that are mediated by chaos and period doubling cascades. While this transition is less evident when intrinsic noise is present in the model, the noisy computational output displays features akin to our experimental results. The differences and similarities observed in the computational and experimental approaches are discussed. 

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5. Trifocal Relative Pose From Lines at Points

   https://doi.org/10.1109/TPAMI.2022.3226165  

   Fabbri, Ricardo; Duff, Timothy; Fan, Hongyi; Regan, Margaret H.; de Pinho, David da; Tsigaridas, Elias; Wampler, Charles W.; Hauenstein, Jonathan D.; Giblin, Peter J.; Kimia, Benjamin; et al (December 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   Abstract—We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of (i) three points and one line and the novel case of (ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Gro ̈bner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver framework MINUS, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We characterize their number of solutions and show with simulated experiments that our solvers are numerically robust and stable under image noise, a key contribution given the borderline intractable degree of nonlinearity of trinocular constraints. We show in real experiments that (i) SIFT feature location and orientation provide good enough point-and-line correspondences for three-view reconstruction and (ii) that we can solve difficult cases with too few or too noisy tentative matches, where the state of the art structure from motion initialization fails. 

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