Notes

In the direction of guessing photosynthetic productivity and biomass get more crop genotypes over the industry time.
The conducted study confirmed the usefulness, effectiveness and robustness of different convolutional architectures in solving melanoma diagnosis problem. Also, important guidelines to researchers working on this area were provided, easing the selection of both the proper convolutional model and technique according the characteristics of data.We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes. In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. For instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information. Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders. We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimer's disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations.Representation learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. Incorporating cognitive ability to automate the camera control enables the surgeon to concentrate more on dealing with surgical instruments. The objective is to reduce the operation time and facilitate the surgery for both surgeons and patients. We propose an end-to-end trainable Spatio-Temporal Multi-Task Learning (ST-MTL) model with a shared encoder and spatio-temporal decoders for the real-time surgical instrument segmentation and task-oriented saliency detection. In the MTL model of shared-parameters, optimizing multiple loss functions into a convergence point is still an open challenge. We tackle the problem with a novel asynchronous spatio-temporal optimization (ASTO) technique by calculating independent gradients for each decoder. We also design a competitive squeeze and excitation unit by casting a skip connection that retains weak features, excites strong features, and performs dynamic spatial and channel-wise feature recalibration. To capture better long term spatio-temporal dependencies, we enhance the long-short term memory (LSTM) module by concatenating high-level encoder features of consecutive frames. We also introduce Sinkhorn regularized loss to enhance task-oriented saliency detection by preserving computational efficiency. We generate the task-aware saliency maps and scanpath of the instruments on the dataset of the MICCAI 2017 robotic instrument segmentation challenge. Compared to the state-of-the-art segmentation and saliency methods, our model outperforms most of the evaluation metrics and produces an outstanding performance in the challenge.The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. read more Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for initializing the subsequent sub-network. The next two sub-networks progressively optimize at the patch level with different resolutions to predict a fine-scale deformation field. Embedding difficulty-aware learning into the hierarchical neural network allows harder patches to be identified in the deeper sub-networks at higher resolutions for refining the deformation field. Experiments conducted on four public datasets validate that our method achieves promising registration accuracy with better preservation of topology, compared with state-of-the-art registration methods.Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on task-specific imaging protocols including a task-specific set of imaging modalities. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from aggregated task-specific hetero-modal domain-shifted and partially-annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper bound of the risk to deal with heterogeneous imaging modalities across datasets. To deal with potential domain shift, we integrated and tested three conventional techniques based on data augmentation, adversarial learning and pseudo-healthy generation. For each individual task, our joint approach reaches comparable performance to task-specific and fully-supervised models. The proposed framework is assessed on two different types of brain lesions White matter lesions and gliomas. In the latter case, lacking a joint ground-truth for quantitative assessment purposes, we propose and use a novel clinically-relevant qualitative assessment methodology.Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification tasks. However, the cost and time for acquiring high quality task-specific large annotated training data are subject to intra- and inter-observer variability, thus challenging the adoption of such tools. To address these challenges, we propose a classification framework via co-representation learning to maximize the learning capability of deep neural networks while using a reduced amount of training data. The framework captures the class-label information and the local spatial distribution information by jointly optimizing a categorical cross-entropy objective and a deep metric learning objective respectively. A deep metric learning objective is incorporated to enhance the classification, especially in the low training data regime. Further, a neighborhood-aware multiple similarity sampling strategy, and a soft-multi-pair objective that optimizes interactions between multiple informative sample pairs, is proposed to accelerate deep metric learning. We evaluate the proposed framework on five benchmark datasets from three digital pathology tasks, i.e., nuclei classification, mitosis detection, and tissue type classification. For all the datasets, our framework achieves state-of-the-art performance when using approximately only 50% of the training data. On using complete training data, the proposed framework outperforms the state-of-the-art on all the five datasets.Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain regions of interest (ROIs) and give insights into gender-related connectional differences. However, to the best of our knowledge, studies on gender differences in brain connectivity were limited to investigating pairwise (i.e., low-order) relationships across ROIs, overlooking the complex high-order interconnectedness of the brain as a network. A few recent works on neurological disorders addressed this limitation by introducing the brain multiplex which is composed of a source network intra-layer, a target intra-layer, and a convolutional interlayer capturing the high-level relationship between both intra-layers. However, brain multiplexes are built from at least two different brain networks hindering their application to connectomic datasets with single brain networks (e.g., functional networks). To fill this gap, we propose Adversarial Brain Multiplex Translator (ABMT), the first work for predicting brain multiplexes from a source network using geometric adversarial learning to investigate gender differences in the human brain. Our framework comprises (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections, (ii) a conditional discriminator which distinguishes between predicted and ground truth target intra-layers, and finally (iii) a multi-layer perceptron (MLP) classifier which supervises the prediction of the target multiplex using the subject class label (e.g., gender). Our experiments on a large dataset demonstrated that predicted multiplexes significantly boost gender classification accuracy compared with source networks and unprecedentedly identify both low and high-order gender-specific brain multiplex connections. Our ABMT source code is available on GitHub at https//github.com/basiralab/ABMT.Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification and lesion-specific segmentation network is proposed for automatic esophageal lesion annotation at pixel level. For the established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.
My Website: https://www.selleckchem.com/products/gsk269962.html