Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Mechanical chest-compression devices: current and future roles

Purpose of review: It is recognised that the quality of CPR is an important predictor of outcome from cardiac arrest yet studies consistently demonstrate that the quality of CPR performed in real life is frequently sub-optimal. Mechanical chest compression devices provide an alternative to manual CPR. This review will consider the evidence and current indications for the use of these devices. Recent findings: Physiological and animal data suggest that mechanical chest compression devices are more effective than manual CPR. However there is no high quality evidence showing improved outcomes in humans. There are specific circumstances where it may not be possible to perform manual CPR effectively e.g. during ambulance transport to hospital, en-route to and during cardiac catheterisation, prior to organ donation and during diagnostic imaging where using these devices may be advantageous. Summary: There is insufficient evidence to recommend the routine use of mechanical chest compression devices. There may be specific circumstances when CPR is difficult or impossible where mechanical devices may play an important role in maintaining circulation. There is an urgent need for definitive clinical and cost effectiveness trials to confirm or refute the place of mechanical chest compression devices during resuscitation