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

Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT) scan is crucial for its effective clinical management. Nodules which are symptomatic of malignancy occupy about 0.0125 - 0.025% of volume in a CT scan of a patient. Manual screening of all slices is a tedious task and presents a high risk of human errors. To tackle this problem we propose a computationally efficient two stage framework. In the first stage, a convolutional neural network (CNN) trained adversarially using Turing test loss segments the lung region. In the second stage, patches sampled from the segmented region are then classified to detect the presence of nodules. The proposed method is experimentally validated on the LUNA16 challenge dataset with a dice coefficient of 0.984±0.0007 for 10-fold cross-validation.

Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss

This paper introduces a noninvasive and labelfree approach for retinal angiography using Laser speckle contrast imaging (LSCI). Retinal vessel structure is segmented using a Hidden Markov Random Field (HMRF) based model. Prior to that, k-means clustering is used to obtain initial parameter set and labels for HMRF. Final parameter set for HMRF is estimated using expectation-maximization (EM) algorithm and final labeling is achieved using maximum aposteriori (MAP) algorithm. Clique energy for HMRF is computed from eigenvalue analysis of structure tensor for each pixel. This helps to get connectivity in the direction of strongest tangents in its neighborhood, facilitating the tracking of fine vessels in retinal vascular network. Quantitative evaluation shows an average vessel segmentation accuracy of 96.41% in normal condition with substantial improvement in tracking capability of fine vessels. Changes in blood flow can be tracked and observed at segmented output; particularly applicable for the study of different pathological conditions.

Probabilistic graphical modeling of speckle statistics in laser speckle contrast imaging for noninvasive and label-free retinal angiography

Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies. Interventional imaging modalities like intravascular ultrasound (IVUS) are used to adjunct diagnosis of atherosclerotic plaques in vessels, and help assess morphological state of the vessel and plaque, which play a significant role for treatment planning. Since speckle intensity in IVUS images are inherently stochastic in nature and challenge clinicians with accurate visibility of the vessel wall boundaries, it requires automation. In this paper we present a method for segmenting the lumen and external elastic laminae of the artery wall in IVUS images using random walks over a multiscale pyramid of Gaussian decomposed frames. The seeds for the random walker are initialized by supervised learning of ultrasonic backscattering and attenuation statistical mechanics from labelled training samples. We have experimentally evaluated the performance using 77 IVUS images acquired at 40 MHz that are available in the IVUS segmentation challenge dataset (http://www.cvc.uab.es/IVUSchallenge2011/dataset.html.) to obtain a Jaccard score of \(0.89 \pm 0.14\) for lumen and \(0.85 \pm 0.12\) for external elastic laminae segmentation over a 10-fold cross-validation study.

Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images Using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks

1 Velmurugan Balasubramanian, School of medical science and Technology, IIT, Kharagpur, India Rajiv Kumar, Department of Chemical Engineering, IIT, Kharagpur, India Sarasa Jyothsna Kamireddi, Department of Electrical Engineering, IIT, Kharagpur, India Rachana Sathish, Department of Electrical Engineering, IIT, Kharagpur, India Debdoot Sheet, Centre of excellence in AI, Department of Electrical Engineering, IIT, Kharagpur, India

/pdf/semantic-segmentation-detection-and-localisation-of-mucosal-50w2sbec3o.pdf

Semantic Segmentation, Detection AND Localisation of Mucosal Lesions from Gastrointestinal Endoscopic Images Using SUMNET.

Now a days video surveillance is becoming more popular due to global security concerns and with the increasing need for effective monitoring of public places. The key goal of video surveillance is to detect suspicious or abnormal behavior. Various efforts have been made to detect an abnormality in the video. Further to these advancements, there is a need for better techniques for evaluation of abnormality localization in video surveillance. Existing technique mainly uses forty percent overlap rule with ground-truth data, and does not considers the extra predicted region into the computation. Existing metrics have been found to be inaccurate when more than one region is present within the frame which may or may not be correctly localized or marked as abnormal. This work attempts to bridge these limitations in existing metrics. In this paper, we investigate three existing metrics and discuss their benefits and limitations for evaluating localization of abnormality in video. We further extend the existing work by introducing penalty functions and substantiate the validity of proposed metrics with a sufficient number of instances. The presented metric are validated on data (35 different situations) for which the overlap has been computed analytically.

Debdoot Sheet

Papers

Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss

Probabilistic graphical modeling of speckle statistics in laser speckle contrast imaging for noninvasive and label-free retinal angiography

Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images Using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks

Semantic Segmentation, Detection AND Localisation of Mucosal Lesions from Gastrointestinal Endoscopic Images Using SUMNET.

Limitations with measuring performance of techniques for abnormality localization in surveillance video and how to overcome them