Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userReconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine
59
0
0.0
(
0
)
Added by
Dimitrios Karkalousos
Publication date
2020
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Objective: To allow efficient learning using the Recurrent Inference Machine (RIM) for image reconstruction whereas not being strictly dependent on the training data distribution so that unseen modalities and pathologies are still accurately recovered. Methods: Theoretically, the RIM learns to solve the inverse problem of accelerated-MRI reconstruction whereas being robust to variable imaging conditions. The efficiency and generalization capabilities with different training datasets were studied, as well as recurrent network units with decreasing complexity: the Gated Recurrent Unit (GRU), the Minimal Gated Unit (MGU), and the Independently Recurrent Neural Network (IndRNN), to reduce inference times. Validation was performed against Compressed Sensing (CS) and further assessed based on data unseen during training. A pathology study was conducted by reconstructing simulated white matter lesions and prospectively undersampled data of a Multiple Sclerosis patient. Results: Training on a single modality of 3T $T_1$-weighted brain data appeared sufficient to also reconstruct 7T $T_{2}^*$-weighted brain and 3T $T_2$-weighted knee data. The IndRNN is an efficient recurrent unit, reducing inference time by 68% compared to CS, whereas maintaining performance. The RIM was able to reconstruct lesions unseen during training more accurately than CS when trained on $T_2$-weighted knee data. Training on $T_1$-weighted brain data and on combined data slightly enhanced the signal compared to CS. Conclusion: The RIM is efficient when decreasing its complexity, which reduces the inference time, whereas still being able to reconstruct data and pathology that was unseen during training.
rate research
Read More
The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation and interpretability issues. These challenges have prevented the broad adaptation of computational pathology for clinical and research purposes. Here we present CLAM - Clustering-constrained attention multiple instance learning, an easy-to-use, high-throughput, and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems. CLAM is a deep-learning-based weakly-supervised method that uses attention-based learning to automatically identify sub-regions of high diagnostic value in order to accurately classify the whole slide, while also utilizing instance-level clustering over the representative regions identified to constrain and refine the feature space. In three separate analyses, we demonstrate the data efficiency and adaptability of CLAM and its superior performance over standard weakly-supervised classification. We demonstrate that CLAM models are interpretable and can be used to identify well-known and new morphological features. We further show that models trained using CLAM are adaptable to independent test cohorts, cell phone microscopy images, and biopsies. CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.
Reconstructing the Gaussian initial conditions at the beginning of the Universe from the survey data in a forward modeling framework is a major challenge in cosmology. This requires solving a high dimensional inverse problem with an expensive, non-linear forward model: a cosmological N-body simulation. While intractable until recently, we propose to solve this inference problem using an automatically differentiable N-body solver, combined with a recurrent networks to learn the inference scheme and obtain the maximum-a-posteriori (MAP) estimate of the initial conditions of the Universe. We demonstrate using realistic cosmological observables that learnt inference is 40 times faster than traditional algorithms such as ADAM and LBFGS, which require specialized annealing schemes, and obtains solution of higher quality.
Cataract is one of the leading causes of reversible visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art artificial intelligence techniques for automatic cataract classification and grading, helping clinicians prevent and treat cataract in time. This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning techniques and deep learning techniques. This paper also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification and grading based on machine learning techniques and present possible solutions to these challenges for future research.
We propose a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features like retinal layers and transverse angiographic vascular features in a joint optimization. Combined with the use of orthogonal scans and favorization of kinematically more plausible displacements, the approach allows subpixel alignment and micrometer-scale distortion correction in all 3 dimensions. As no specific structures or layers are segmented, the approach is by design robust to pathologic changes. It is furthermore designed for highly parallel implementation and brief runtime, allowing its integration in clinical routine even for high density or wide-field scans. We evaluated the algorithm with metrics related to clinically relevant features in a large-scale quantitative evaluation based on 204 volumetric scans of 17 subjects including both a wide range of pathologies and healthy controls. Using this method, we achieve state-of-the-art axial performance and show significant advances in both transverse co-alignment and distortion correction, especially in the pathologic subgroup.
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of supervised learning strategies. To address these challenges, we propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate spatio-temporal information across frames and reduces the need for high complexity networks. Our setting enables learning from unpaired videos in a cyclic adversarial manner, where the proposed recurrent units are employed in all architectures. Efficient training is accomplished by introducing one single discriminator that learns the joint distribution of source and target domain simultaneously. The enhancement results demonstrate clear superiority of the proposed video enhancer over the state-of-the-art methods, in all terms of visual quality, quantitative metrics, and inference speed. Notably, our video enhancer is capable of enhancing over 35 frames per second of FullHD video (1080x1920).
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
