A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos

Detecting breast lesion in videos is crucial for computer-aided diagnosis. Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone features based on the self-attention operation. We argue that such a strategy struggles to effectively perform deep feature aggregation and ignores the useful local information. To tackle these issues, we propose a spatial-temporal deformable attention based framework, named STNet. Our STNet introduces a spatial-temporal deformable attention module to perform local spatial-temporal feature fusion. The spatial-temporal deformable attention module enables deep feature aggregation in each stage of both encoder and decoder. To further accelerate the detection speed, we introduce an encoder feature shuffle strategy for multi-frame prediction during inference. In our encoder feature shuffle strategy, we share the backbone and encoder features, and shuffle encoder features for decoder to generate the predictions of multiple frames. The experiments on the public breast lesion ultrasound video dataset show that our STNet obtains a state-of-the-art detection performance, while operating twice as fast inference speed. The code and model are available at https://github.com/AlfredQin/STNet.Comment: Accepted by MICCAI 202

Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review

Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures. However, obtaining reliable and accurate medical image annotations often necessitates expert guidance, making the acquisition of large-scale annotated datasets challenging and costly. This presents obstacles for traditional supervised learning methods. Consequently, semi-supervised learning (SSL) has emerged as a promising solution, capable of utilizing unlabeled data to enhance model performance and has been widely adopted in medical image segmentation tasks. However, striking a balance between segmentation accuracy and inference speed remains a challenge for real-time segmentation. This paper provides a comprehensive review of research progress in real-time intelligent semi-supervised ultrasound video object segmentation (SUVOS) and offers insights into future developments in this area

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

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces