Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant cost. Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process. In this paper, we propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples. AGILE combines a meta-learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. It then uses Bayesian dropout uncertainty estimates to actively select the most difficult samples when updating the model to a new task. This allows AGILE to learn with fewer tasks and a few informative samples, achieving high performance with a limited dataset. We perform our experiments using the brain cell classification task and compare the results to a plain meta-learning model trained from scratch. We show that the proposed task-augmented meta-learning framework can learn to classify new cell types after a single gradient step with a limited number of training samples. We show that active learning with Bayesian uncertainty can further improve the performance when the number of training samples is extremely small. Using only 1% of the training data and a single update step, we achieved 90% accuracy on the new cell type classification task, a 50% points improvement over a state-of-the-art meta-learning algorithm.
Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported. however, marginal results when analyzing organs in the op…
University College of Goizha（戈伊扎大学学院）；Sulaimani Polytechnic University（苏莱曼尼理工大学）；Kurdistan Institution for Strategic Studies and Scientific Research（库尔德斯坦战略研究与科学研究所）；University of Kurdistan Hewler（库尔德斯坦休勒大学）
Automated brain tumor detection is becoming a highly considerable medical diagnosis research. In recent medical diagnoses, detection and classification are highly considered to employ machine learning and deep learning techniques. Nevertheless, the accuracy and performance of current models need to be improved for suitable treatments. In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered. This hybridiz…
Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine learning approaches have been employed to automatically predict synaptic clefts from EM images. In this work, we propose a novel and augmented deep learning model, known as CleftNet, for improving synaptic cleft detection from brain EM images. We first propose two novel network components, known as the feature augmentor and the label augmentor, for aug…
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机学院航空航天地海一体化大数据应用技术国家工程实验室）；Imperial College London（帝国理工学院）
DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）
The Hong Kong University of Science and Technology（香港科技大学）
There is a common belief that the successful training of deep neural networks requires many annotated training samples, which are often expensive and difficult to obtain especially in the biomedical imaging field. While it is often easy for researchers to use data augmentation to expand the size of training sets, constructing and generating generic augmented data that is able to teach the network the desired invariance and robustness properties using traditional data augmentation techniques is challenging in practice. In this paper, we propose a novel automatic data augmentation method that us…
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segment…
The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset
University of California San Francisco（加州大学旧金山分校）；University of California, San Diego（加州大学圣地亚哥分校）；University of Pennsylvania（宾夕法尼亚大学）；Duke University（杜克大学）
Vanderbilt University（范德堡大学）；Henry Jackson Foundation（亨利·杰克逊基金会）；University of Calgary（卡尔加里大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) approach has been shown to effectively segment whole brain non-contrast T1w MRI with 132 volumetric labels. Enhancing generalizability of SLANT would enable broader application of volumetric assessment in multi-site studies. Transfer learning (TL) is commonly used to update the neural network weights for local factors; yet, it is commonly recognized to risk d…
University Hospital Erlangen（埃尔朗根大学医院）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心）；Pattern Recognition Lab（模式识别实验室）
Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imaging and receive salvage treatments in case of significant tumor growth. We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment. All experiments are performed on a dataset from clinical routine…
Universitätsklinikum Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心（CCC ER-EMN））
Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting t…
Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
American International University-Bangladesh（美国国际大学-孟加拉国）；Asian University of Bangladesh（孟加拉国亚洲大学）；Information and Computer Science Department, King Fahd University of Petroleum and Minerals (KFUPM)（石油和矿物大学（KFUPM）信息与计算机科学系）；SDAIA-KFUPM Joint Research Center for AI, King Fahd University of Petroleum and Minerals（人工智能联合研究中心（SDAIA-KFUPM）, 石油和矿物大学）；Interdisciplinary Research Center for Bio Systems and Machines, King Fahd University of Petroleum and Minerals（生物系统与机器交叉研究中心，石油和矿物大学）；Independent University, Bangladesh（独立大学）
German Research Center for Artificial Intelligence, DFKI GmbH, Robotics Innovation Center（德国人工智能研究中心，DFKI有限公司，机器人创新中心）
On image data, data augmentation is becoming less relevant due to the large amount of available training data and regularization techniques. Common approaches are moving windows (cropping), scaling, affine distortions, random noise, and elastic deformations. For electroencephalographic data, the lack of sufficient training data is still a major issue. We suggest and evaluate different approaches to generate augmented data using temporal and spatial/rotational distortions. Our results on the perception of rare stimuli (P300 data) and movement prediction (MRCP data) show that these approaches ar…
ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET
The Chinese University of Hong Kong, HKSAR（香港中文大学）；TReNDS Center (Georgia State, Georgia Tech, Emory)（TReNDS中心（佐治亚州立大学、佐治亚理工学院、埃默里大学））；ShanghaiTech University, Shanghai, P.R.China（上海科技大学）；University of California, Berkeley, USA（加州大学伯克利分校）；University of Texas Health Science Center at Houston, USA（德克萨斯大学健康科学中心（休斯顿））；Nanyang Technological University, Singapore（南洋理工大学）
Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable. We study a novel and practical problem of Open Domain Generalization (OpenDG), which learns from different source domains to achieve high performance on an unknown target domain, where the distributions and label sets of each individual source domain and the target domain can be different. The problem can be generally applied to diverse source domains and widely applicable to real-world applicatio…
Oslo University Hospital（奥斯陆大学医院）；Stanford University（斯坦福大学）
The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI sequences using an input dropout layer, thus simulating the scenario of missing MRI data by training on the full set and all possible subsets of the input data. This retrospective, multi-center study, evaluated 165 patients with brain metastases. A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested …
Korea Advanced Institute of Science and Technology (KAIST)（韩国高等科学技术研究院（KAIST））
Medical image analysis using deep neural networks has been actively studied. Deep neural networks are trained by learning data. For accurate training of deep neural networks, the learning data should be sufficient, of good quality, and should have a generalized property. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients' privacy. In comparison, the medical images of healthy volunteers can be easily acquired. Using healthy brain images, the …
National Institute of Informatics（国立信息学研究所）；National Center for Global Health and Medicine（国立国际医疗研究中心）；University of Cambridge（剑桥大学）；The University of Tokyo（东京大学）
Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize additional training data to handle the small/fragmented medical imaging datasets collected from various scanners; those images are realistic but completely different from the original ones, filling the data lack in the real image distribution. However, we cannot easily use them to locate disease areas, considering expert physicians' ex…
Stanford University（斯坦福大学）；Oslo University Hospital（奥斯陆大学医院）
Deep learning has proven to be an essential tool for medical image analysis. However, the need for accurately labeled input data, often requiring time- and labor-intensive annotation by experts, is a major limitation to the use of deep learning. One solution to this challenge is to allow for use of coarse or noisy labels, which could permit more efficient and scalable labeling of images. In this work, we develop a lopsided loss function based on entropy regularization that assumes the existence of a nontrivial false negative rate in the target annotations. Starting with a carefully annotated b…
SLIMBRAIN: Augmented Reality Real-Time Acquisition and Processing System For Hyperspectral Classification Mapping with Depth Information for In-Vivo Surgical Procedures
Research Center on Software Technologies and Multimedia Systems, Universidad Politécnica de Madrid（马德里理工大学软件技术与多媒体系统研究中心）；Department of Neurosurgery, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid（马德里康普顿斯大学十月十二日大学医院神经外科）
SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI Data Generation in Young People
Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院布里格姆医疗系统）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Maastricht University（马斯特里赫特大学）；Boston Children's Hospital（波士顿儿童医院）
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Artificial intelligence (AI) in radiology has made great strides in recent years, but many hurdles remain. Overfitting and lack of generalizability represent important ongoing challenges hindering accurate and dependable clinical deployment. If AI algorithms can avoid overfitting and achieve true generalizability, they can go from the research realm to the forefront of clinical work. Recently, small data AI approaches such as deep neuroevolution (DNE) have avoided overfitting small training sets. We seek to address both overfitting and generalizability by applying DNE to a virtually pooled dat…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Purpose: A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RECIST or RANO criteria, is tedious, time-consuming, and can miss important tumor response information; most notably, they exclude non-target lesions. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Due to often low patient enrolments in clinical trials, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) c…
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framewor…
