Computer aided diagnosis (CAD) of Breast Cancer (BRCA) images has been an active area of research in recent years. The main goals of this research is to develop reliable automatic methods for detecting and diagnosing different types of BRCA from diagnostic images. In this paper, we present a review of the state of the art CAD methods applied to magnetic resonance (MRI) and mammography images of BRCA patients. The review aims to provide an extensive introduction to different features extracted from BRCA images through texture and statistical analysis and to categorize deep learning frameworks and data structures capable of using metadata to aggregate relevant information to assist oncologists and radiologists. We divide the existing literature according to the imaging modality and into radiomics, machine learning, or combination of both. We also emphasize the difference between each modality and methods strengths and weaknesses and analyze their performance in detecting BRCA through a quantitative comparison. We compare the results of various approaches for implementing CAD systems for the detection of BRCA. Each approachs standard workflow components are reviewed and summary tables provided. We present an extensive literature review of radiomics feature extraction techniques and machine learning methods applied in BRCA diagnosis and detection, focusing on data preparation, data structures, pre processing and post processing strategies available in the literature. There is a growing interest on radiomic feature extraction and machine learning methods for BRCA detection through histopathological images, MRI and mammography images. However, there isnt a CAD method able to combine distinct data types to provide the best diagnostic results. Employing data fusion techniques to medical images and patient data could lead to improved detection and classification results.
Physics-based generation of multilayer corneal OCT data via Gaussian modeling and MCML for AI-driven diagnostic and surgical guidance applications
Department of Electrical and Computer Engineering, Johns Hopkins University（电气与计算机工程系，约翰霍普金斯大学）；Johns Hopkins University（约翰霍普金斯大学）；Wilmer Eye Institute, Johns Hopkins University（威尔默眼科研究所，约翰霍普金斯大学）；Vision Institute, Sorbonne University, INSERM, CNRS（视觉研究所，索邦大学，INSERM，CNRS）
California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural netw…
LLM-Guided Diagnostic Evidence Alignment for Medical Vision-Language Pretraining under Limited Pairing
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学阿利安斯商学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Johns Hopkins University（约翰霍普金斯大学）；Applied Physics Laboratory（应用物理实验室）；University of Virginia（弗吉尼亚大学）
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic. In contrast to most recent work in privacy-aware ML that has been focused on model alteration and post-processing steps, we propose here a novel and complementary scheme that enhances the security of medical data by controlling the data sharing process. We develop and evaluate a privacy defense protoc…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
A common problem with segmentation of medical images using neural networks is the difficulty to obtain a significant number of pixel-level annotated data for training. To address this issue, we proposed a semi-supervised segmentation network based on contrastive learning. In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs, respectively, to formulate the learning as solving a MMS problem. The all-negative pairs…
In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make…
Virginia Tech（弗吉尼亚理工大学）；University of Virginia（弗吉尼亚大学）
Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a …
Institute Mihajlo Pupin, University of Belgrade（贝尔格莱德大学米哈伊洛·普平研究所）；Center for Pathology and Forensics of the Military Medical Academy in Belgrade（贝尔格莱德军事医学科学院病理与法医中心）；University of Siegen（锡根大学）；Wireill；The School of Computing, University Union AB（AB大学联盟计算机学院）
This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from a normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability
University College London（伦敦大学学院）；Katholieke Universiteit Leuven（荷语天主教鲁汶大学）；University College London Hospitals（伦敦大学学院医院）；Universitair Ziekenhuis Leuven（鲁汶大学医院）
Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FCN) and optical flow tracking. Our method exploits the ability of deep neural networks to produce accurate segmentations of highly deformable parts along with the high speed of optical flow. Furthermore, the pre-trained FCN can be fine-tuned on a small amount of medical images without the need to hand-craft features. We validated our method using existing and new benchmark datasets, covering both ex vivo and in vivo r…
National University of Singapore（新加坡国立大学）；Imperial College London（帝国理工学院）；National Institute of Technology, Tiruchirappalli（蒂鲁吉拉伯利国家理工学院）；Chinese University of Hong Kong（香港中文大学）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究所）
Visual question answering (VQA) in surgery is largely unexplored. Expert surgeons are scarce and are often overloaded with clinical and academic workloads. This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures. At times, students and junior residents also refrain from asking too many questions during classes to reduce disruption. While computer-aided simulators and recording of past surgical procedures have been made available for them to observe and improve their skills, they still hugely rely on medic…
Purdue University（普渡大学）；Madigan Army Medical Center（马迪根陆军医疗中心）
Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. Nonetheless, expert mentors are often unavailable to provide trainees with real-time medical guidance. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. However, AI/autonomous mentoring in medicine has been limited by the availability of generalizable prediction models, and surgical procedures datasets to train those models with. This work presents the initial steps towards the developm…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Deep neural networks are discovered to be non-robust when attacked by imperceptible adversarial examples, which is dangerous for it applied into medical diagnostic system that requires high reliability. However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks. The preprocessing methods (e.g., random resizing, compression) may lead to the loss of the small lesions feature in the medical image. Retraining the network on the augmented data set is also not practical for medical models that have already been deployed online. Accordingly, …
University of Aberdeen, UK（阿伯丁大学，英国）；Cambridge Research Laboratory, Toshiba Europe Ltd, UK（剑桥研究实验室，东芝欧洲有限公司，英国）；Nepal Applied Mathematics and Informatics Institute for research (Naamii), Nepal（尼泊尔应用数学与信息学研究所（Naamii），尼泊尔）；University of Lausanne, Switzerland（洛桑大学，瑞士）
Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Surgical Artificial Intelligence Laboratory, Massachusetts General Hospital, MA, USA（马萨诸塞州总医院手术人工智能实验室）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, MA, USA（麻省理工学院计算机科学与人工智能实验室）；Department of Surgery, Duke University, NC, USA（达特茅斯大学外科系）
Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model
Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Shanghai Jiao Tong University, China（上海交通大学）；Monash University, Australia（墨尔本大学）；East China Normal University, China（华东师范大学）；Eye Hospital, Wenzhou Medical University, China（温州医学院眼医院）；Shanghai Academy of Artificial Intelligence for Science, China（上海人工智能科学研究院）；Imperial College London, UK（伦敦帝国理工学院）；Shanghai Innovation Institute, China（上海创新研究院）
Tecnologico de Monterrey（蒙特雷科技大学）；University of Oxford（牛津大学）；Oxford NIHR Biomedical Research Centre（牛津国家卫生研究院生物医学研究中心）
Precise instrument segmentation aid surgeons to navigate the body more easily and increase patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 cha…
LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）；The University of Sydney（悉尼大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions. However, taken medical images especially throat and endoscopy images are normally hazy, lack of focus, or uneven illumination. Thus, these could difficult the diagnosis process for doctors. In this paper, we propose MIINet, a novel image-to-image translation network for improving quality of medical images by unsupervised translating low-quality images to the high-quality clean version. Our MIINet is not only capable of generating high-resolution clean images, but also preservi…
Mannheim University of Applied Sciences（曼海姆应用科学大学）；Magdeburg University（马格德堡大学）；Heidelberg University Hospital（海德堡大学医院）
Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, …
