There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.

/pdf/clinically-applicable-deep-learning-for-diagnosis-and-56r182cw0z.pdf

Clinically applicable deep learning for diagnosis and referral in retinal disease

Designing Deep Neural High-Density Compression Engines for Radiology Images

Radiologists use various imaging modalities to aid in different tasks like diagnosis of disease, lesion visualization, surgical planning and prognostic evaluation. Most of these tasks rely on the the accurate delineation of the anatomical morphology of the organ, lesion or tumor. Deep learning frameworks can be designed to facilitate automated delineation of the region of interest in such cases with high accuracy. Performance of such automated frameworks for medical image segmentation can be improved with efficient integration of information from multiple modalities aided by suitable learning strategies. In this direction, we show the effectiveness of residual network trained adversarially in addition to a boundary weighted loss. The proposed methodology is experimentally verified on the SPES-ISLES 2015 dataset for ischaemic stroke segmentation with an average Dice coefficient of $0.881$ for penumbra and $0.877$ for core. It was observed that addition of residual connections and boundary weighted loss improved the performance significantly.

/pdf/significance-of-residual-learning-and-boundary-weighted-loss-54jm91fqct.pdf

Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation

Convolutional neural networks (CNN) are generally designed with a heuristic initialization of network architecture and trained for a certain task. This often leads to overparametrization after learning and induces redundancy in the information flow paths within the network. This robustness and reliability is at the increased cost of redundant computations. Several methods have been proposed which leverage metrics that quantify the redundancy in each layer. However, layer-wise evaluation in these methods disregards the long-range redundancy which exists across depth on account of the distributed nature of the features learned by the model. In this paper, we propose (i) a mechanism to empirically demonstrate the robustness in performance of a CNN on account of redundancy across its depth, (ii) a method to identify the systemic redundancy in response of a CNN across depth using the understanding of unit impulse response, we subsequently demonstrate use of these methods to interpret redundancy in few networks as example. These techniques provide better insights into the internal dynamics of a CNN

https://openaccess.thecvf.com/content_CVPRW_2019/papers/Explainable AI/Sathish_Unit_Impulse_Response_as_an_Explainer_of_Redundancy_in_a_CVPRW_2019_paper.pdf

Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network

The present invention relates to a multispectral optical imaging device, comprising; LED based structured multispectral illumination system; a driver circuit that triggers plurality of LEDs at different spectrum of illumination; a user-based platform/interface distributed over cloud that facilitates the communication between said device and a user; and a computational imaging server to process images according to server requirement and transfers the inferences back to device. The method of operation comprises the steps of actuating a camera by the user platform/interface on a smart-phone or the imaging sensor on the standalone device for image acquisition; transferring acquired multispectral images to a cloud based computational imaging server for processing of images; and transferring back clinical inferences to the device for clinical assistance and future reference.

Multispectral optical imaging device and method for contactless functional imaging of skin

Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cancer related deaths in women in India and other low and middle income countries (LMICs) especially among the urban poor and slum dwellers. Several sophisticated techniques such as cytology tests, HPV tests etc. have been widely used for screening of cervical cancer. These tests are inherently time consuming. In this paper, we propose a convolutional autoencoder based framework, having an architecture similar to SegNet which is trained in an adversarial fashion for classifying images of the cervix acquired using a colposcope. We validate performance on the Intel-Mobile ODT cervical image classification dataset. The proposed method outperforms the standard technique of fine-tuning convolutional neural networks pre-trained on ImageNet database with an average accuracy of 73.75%.

Debdoot Sheet

Papers

Designing Deep Neural High-Density Compression Engines for Radiology Images

Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation

Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network

Multispectral optical imaging device and method for contactless functional imaging of skin

Identification of Cervical Pathology using Adversarial Neural Networks.