“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

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Deformable Medical Image Registration: A Survey

The International Liaison Committee on Resuscitation (ILCOR) Advanced Life Support (ALS) Task Force performed detailed systematic reviews based on the recommendations of the Institute of Medicine of the National Academies1 and using the methodological approach proposed by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) Working Group.2 Questions to be addressed (using the PICO [population, intervention, comparator, outcome] format)3 were prioritized by ALS Task Force members (by voting). Prioritization criteria included awareness of significant new data and new controversies or questions about practice. Questions about topics no longer relevant to contemporary practice or where little new research has occurred were given lower priority. The ALS Task Force prioritized 42 PICO questions for review. With the assistance of information specialists, a detailed search for relevant articles was performed in each of 3 online databases (PubMed, Embase, and the Cochrane Library).

By using detailed inclusion and exclusion criteria, articles were screened for further evaluation. The reviewers for each question created a reconciled risk of bias assessment for each of the included studies, using state-of-the-art tools: Cochrane for randomized controlled trials (RCTs),4 Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 for studies of diagnostic accuracy,5 and GRADE for observational studies that inform both therapy and prognosis questions.6

GRADE evidence profile tables7 were then created to facilitate an evaluation of the evidence in support of each of the critical and important outcomes. The quality of the evidence (or confidence in the estimate of the effect) was categorized as high, moderate, low, or very low,8 based on the study methodologies and the 5 core GRADE domains of risk of bias, inconsistency, indirectness, imprecision, and other considerations (including publication bias).9

These evidence profile tables were then used to create a …

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Part 4: Advanced life support: 2015 International consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations

Medical Image Computing and Computer-Assisted Intervention

Generative adversarial network in medical imaging: A review.

Deep Vessel Segmentation By Learning Graphical Connectivity

Color image segmentation based on 3-D clustering: morphological approach

We propose a framework for localization and classification of masses in breast ultrasound images. We have experimentally found that training convolutional neural network-based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets. To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner. We propose a systematic weakly and semi-supervised training scenario with appropriate training loss selection. Experimental results show that the proposed method can successfully localize and classify masses with less annotation effort. The results trained with only 10 strongly annotated images along with weakly annotated images were comparable to results trained from 800 strongly annotated images, with the 95% confidence interval (CI) of difference −3%–5%, in terms of the correct localization (CorLoc) measure, which is the ratio of images with intersection over union with ground truth higher than 0.5. With the same number of strongly annotated images, additional weakly annotated images can be incorporated to give a 4.5% point increase in CorLoc, from 80% to 84.50% (with 95% CIs 76%–83.75% and 81%–88%). The effects of different algorithmic details and varied amount of data are presented through ablative analysis.

Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images

The availability of accurate depth cameras have made real-time human pose estimation possible; however, there are still demands for faster algorithms on low power processors. This paper introduces 1000 frames per second pose estimation method on a single core CPU. A large computation gain is achieved by random walk sub-sampling. Instead of training trees for pixel-wise classification, a regression tree is trained to estimate the probability distribution to the direction toward the particular joint, relative to the current position. At test time, the direction for the random walk is randomly chosen from a set of representative directions. The new position is found by a constant step toward the direction, and the distribution for next direction is found at the new position. The continual random walk through 3D space will eventually produce an expectation of step positions, which we estimate as the joint position. A regression tree is built separately for each joint. The number of random walk steps can be assigned for each joint so that the computation time is consistent regardless of the size of body segmentation. The experiments show that even with large computation gain, the accuracy is higher or comparable to the state-of-the-art pose estimation methods.

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Random tree walk toward instantaneous 3D human pose estimation

Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.

Il Dong Yun

Papers

Deep Vessel Segmentation By Learning Graphical Connectivity

Color image segmentation based on 3-D clustering: morphological approach

Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images

Random tree walk toward instantaneous 3D human pose estimation

Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound.