journal
Journals Machine Learning in Medical Im...

Machine Learning in Medical Imaging

https://read.qxmd.com/read/38463442/class-balanced-deep-learning-with-adaptive-vector-scaling-loss-for-dementia-stage-detection
#1
JOURNAL ARTICLE
Boning Tong, Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Jason Moore, Marylyn Ritchie, Li Shen
Alzheimer's disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net...
2024: Machine Learning in Medical Imaging
https://read.qxmd.com/read/38390519/deep-bayesian-quantization-for-supervised-neuroimage-search
#2
JOURNAL ARTICLE
Erkun Yang, Cheng Deng, Mingxia Liu
Neuroimage retrieval plays a crucial role in providing physicians with access to previous similar cases, which is essential for case-based reasoning and evidence-based medicine. Due to low computation and storage costs, hashing-based search techniques have been widely adopted for establishing image retrieval systems. However, these methods often suffer from nonnegligible quantization loss, which can degrade the overall search performance. To address this issue, this paper presents a compact coding solution namely Deep Bayesian Quantization (DBQ), which focuses on deep compact quantization that can estimate continuous neuroimage representations and achieve superior performance over existing hashing solutions...
October 2023: Machine Learning in Medical Imaging
https://read.qxmd.com/read/38389805/structural-mri-harmonization-via-disentangled-latent-energy-based-style-translation
#3
JOURNAL ARTICLE
Mengqi Wu, Lintao Zhang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, Mingxia Liu
Multi-site brain magnetic resonance imaging (MRI) has been widely used in clinical and research domains, but usually is sensitive to non-biological variations caused by site effects ( e.g. , field strengths and scanning protocols). Several retrospective data harmonization methods have shown promising results in removing these non-biological variations at feature or whole-image level. Most existing image-level harmonization methods are implemented through generative adversarial networks, which are generally computationally expensive and generalize poorly on independent data...
October 2023: Machine Learning in Medical Imaging
https://read.qxmd.com/read/38274402/radiomics-boosts-deep-learning-model-for-ipmn-classification
#4
JOURNAL ARTICLE
Lanhong Yao, Zheyuan Zhang, Ugur Demir, Elif Keles, Camila Vendrami, Emil Agarunov, Candice Bolan, Ivo Schoots, Marc Bruno, Rajesh Keswani, Frank Miller, Tamas Gonda, Cemal Yazici, Temel Tirkes, Michael Wallace, Concetto Spampinato, Ulas Bagci
Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans...
October 2023: Machine Learning in Medical Imaging
https://read.qxmd.com/read/37854585/ia-gcn-interpretable-attention-based-graph-convolutional-network-for-disease-prediction
#5
JOURNAL ARTICLE
Anees Kazi, Soroush Farghadani, Iman Aganj, Nassir Navab
Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in general in computer vision; yet, in the medical domain, it requires further examination. Most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the output of the model in a post - hoc fashion. In this paper, we propose an interpretable attention module (IAM) that explains the relevance of the input features to the classification task on a GNN Model. The model uses these interpretations to improve its performance...
October 2023: Machine Learning in Medical Imaging
https://read.qxmd.com/read/38107539/spherical-transformer-on-cortical-surfaces
#6
JOURNAL ARTICLE
Jiale Cheng, Xin Zhang, Fenqiang Zhao, Zhengwang Wu, Xinrui Yuan, John H Gilmore, Li Wang, Weili Lin, Gang Li
Motivated by the recent great success of attention modeling in computer vision, it is highly desired to extend the Transformer architecture from the conventional Euclidean space to non-Euclidean spaces. Given the intrinsic spherical topology of brain cortical surfaces in neuroimaging, in this study, we propose a novel Spherical Transformer, an effective general-purpose backbone using the self-attention mechanism for analysis of cortical surface data represented by triangular meshes. By mapping the cortical surface onto a sphere and splitting it uniformly into overlapping spherical surface patches, we encode the long-range dependency within each patch by the self-attention operation and formulate the cross-patch feature transmission via overlapping regions...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/37126478/harmonization-of-multi-site-cortical-data-across-the-human-lifespan
#7
JOURNAL ARTICLE
Sahar Ahmad, Fang Nan, Ye Wu, Zhengwang Wu, Weili Lin, Li Wang, Gang Li, Di Wu, Pew-Thian Yap
Neuroimaging data harmonization has become a prerequisite in integrative data analytics for standardizing a wide variety of data collected from multiple studies and enabling interdisciplinary research. The lack of standardized image acquisition and computational procedures introduces non-biological variability and inconsistency in multi-site data, complicating downstream statistical analyses. Here, we propose a novel statistical technique to retrospectively harmonize multi-site cortical data collected longitudinally and cross-sectionally between birth and 100 years...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36780251/dynamic-linear-transformer-for-3d-biomedical-image-segmentation
#8
JOURNAL ARTICLE
Zheyuan Zhang, Ulas Bagci
Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism. However, most methods are still designed for 2D medical images while ignoring the essential 3D volume information. The main challenge for 3D Transformer-based segmentation methods is the quadratic complexity introduced by the self-attention mechanism [17]. In this paper, we are addressing these two research gaps, lack of 3D methods and computational complexity in Transformers, by proposing a novel Transformer architecture that has an encoder-decoder style architecture with linear complexity...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36656619/multi-scale-multi-structure-siamese-network-mmsnet-for-primary-open-angle-glaucoma-prediction
#9
JOURNAL ARTICLE
Mingquan Lin, Lei Liu, Mae Gorden, Michael Kass, Sarah Van Tassel, Fei Wang, Yifan Peng
Primary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. POAG prediction before onset plays an important role in early treatment. Although deep learning methods have been proposed to predict POAG, these methods mainly focus on current status prediction. In addition, all these methods used a single image as input. On the other hand, glaucoma specialists determine a glaucomatous eye by comparing the follow-up optic nerve image with the baseline along with supplementary clinical data...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36656604/predicting-age-related-macular-degeneration-progression-with-longitudinal-fundus-images-using-deep-learning
#10
JOURNAL ARTICLE
Junghwan Lee, Tingyi Wanyan, Qingyu Chen, Tiarnan D L Keenan, Benjamin S Glicksberg, Emily Y Chew, Zhiyong Lu, Fei Wang, Yifan Peng
Accurately predicting a patient's risk of progressing to late age-related macular degeneration (AMD) is difficult but crucial for personalized medicine. While existing risk prediction models for progression to late AMD are useful for triaging patients, none utilizes longitudinal color fundus photographs (CFPs) in a patient's history to estimate the risk of late AMD in a given subsequent time interval. In this work, we seek to evaluate how deep neural networks capture the sequential information in longitudinal CFPs and improve the prediction of 2-year and 5-year risk of progression to late AMD...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36594909/fast-image-level-mri-harmonization-via-spectrum-analysis
#11
JOURNAL ARTICLE
Hao Guan, Siyuan Liu, Weili Lin, Pew-Thian Yap, Mingxia Liu
Pooling structural magnetic resonance imaging (MRI) data from different imaging sites helps increase sample size to facilitate machine learning based neuroimage analysis, but usually suffers from significant cross-site and/or cross-scanner data heterogeneity. Existing studies often focus on reducing cross-site and/or cross-scanner heterogeneity at handcrafted feature level targeting specific tasks (e.g., classification or segmentation), limiting their adaptability in clinical practice. Research on image-level MRI harmonization targeting a broad range of applications is very limited...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36594904/understanding-clinical-progression-of-late-life-depression-to-alzheimer-s-disease-over-5-years-with-structural-mri
#12
JOURNAL ARTICLE
Lintao Zhang, Minhui Yu, Lihong Wang, David C Steffens, Rong Wu, Guy G Potter, Mingxia Liu
Previous studies have shown that late-life depression (LLD) may be a precursor of neurodegenerative diseases and may increase the risk of dementia. At present, the pathological relationship between LLD and dementia, in particularly Alzheimer's disease (AD) is unclear. Structural MRI (sMRI) can provide objective biomarkers for the computer-aided diagnosis of LLD and AD, providing a promising solution to understand the clinical progression of brain disorders. But few studies have focused on sMRI-based predictive analysis of clinical progression from LLD to AD...
September 2022: Machine Learning in Medical Imaging
https://read.qxmd.com/read/37808083/knowledge-guided-multiview-deep-curriculum-learning-for-elbow-fracture-classification
#13
JOURNAL ARTICLE
Jun Luo, Gene Kitamura, Dooman Arefan, Emine Doganay, Ashok Panigrahy, Shandong Wu
Elbow fracture diagnosis often requires patients to take both frontal and lateral views of elbow X-ray radiographs. In this paper, we propose a multiview deep learning method for an elbow fracture subtype classification task. Our strategy leverages transfer learning by first training two single-view models, one for frontal view and the other for lateral view, and then transferring the weights to the corresponding layers in the proposed multiview network architecture. Meanwhile, quantitative medical knowledge was integrated into the training process through a curriculum learning framework, which enables the model to first learn from "easier" samples and then transition to "harder" samples to reach better performance...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36780259/hierarchical-3d-feature-learning-for-pancreas-segmentation
#14
JOURNAL ARTICLE
Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci, Simone Palazzo, Ulas Bagci, Concetto Spampinato
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to multiple 3D decoders that individually predict intermediate segmentation maps. Finally, all segmentation maps are combined to obtain a unique detailed segmentation mask. We test our model on both CT and MRI imaging data: the publicly available NIH Pancreas-CT dataset (consisting of 82 contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans)...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/36780256/information-bottleneck-attribution-for-visual-explanations-of-diagnosis-and-prognosis
#15
JOURNAL ARTICLE
Ugur Demir, Ismail Irmakci, Elif Keles, Ahmet Topcu, Ziyue Xu, Concetto Spampinato, Sachin Jambawalikar, Evrim Turkbey, Baris Turkbey, Ulas Bagci
Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/35695860/seeking-an-optimal-approach-for-computer-aided-pulmonary-embolism-detection
#16
JOURNAL ARTICLE
Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou, Michael B Gotway, Jianming Liang
Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE. However, numerous competing methods for a given task in the deep learning literature exist, causing great confusion regarding the development of a CAD PE system...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/35647616/improving-joint-learning-of-chest-x-ray-and-radiology-report-by-word-region-alignment
#17
JOURNAL ARTICLE
Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber, Sargur N Srihari, Yifan Peng, Mingchen Gao
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching. Both are bidirectionally constrained on Cross-Entropy based and ranking-based Triplet Matching Losses...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/35528703/multi-scale-self-supervised-learning-for-multi-site-pediatric-brain-mr-image-segmentation-with-motion-gibbs-artifacts
#18
JOURNAL ARTICLE
Yue Sun, Kun Gao, Weili Lin, Gang Li, Sijie Niu, Li Wang
Accurate tissue segmentation of large-scale pediatric brain MR images from multiple sites is essential to characterize early brain development. Due to imaging motion/Gibbs artifacts and multi-site issue (or domain shift issue), it remains a challenge to accurately segment brain tissues from multi-site pediatric MR images. In this paper, we present a multi-scale self-supervised learning (M-SSL) framework to accurately segment tissues for multi-site pediatric brain MR images with artifacts. Specifically, we first work on the downsampled images to estimate coarse tissue probabilities and build a global anatomic guidance...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/34964046/skullengine-a-multi-stage-cnn-framework-for-collaborative-cbct-image-segmentation-and-landmark-detection
#19
JOURNAL ARTICLE
Qin Liu, Han Deng, Chunfeng Lian, Xiaoyang Chen, Deqiang Xiao, Lei Ma, Xu Chen, Tianshu Kuang, Jaime Gateno, Pew-Thian Yap, James J Xia
Accurate bone segmentation and landmark detection are two essential preparation tasks in computer-aided surgical planning for patients with craniomaxillofacial (CMF) deformities. Surgeons typically have to complete the two tasks manually, spending ~12 hours for each set of CBCT or ~5 hours for CT. To tackle these problems, we propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models...
September 2021: Machine Learning in Medical Imaging
https://read.qxmd.com/read/34927174/skull-segmentation-from-cbct-images-via-voxel-based-rendering
#20
JOURNAL ARTICLE
Qin Liu, Chunfeng Lian, Deqiang Xiao, Lei Ma, Han Deng, Xu Chen, Dinggang Shen, Pew-Thian Yap, James J Xia
Skull segmentation from three-dimensional (3D) cone-beam computed tomography (CBCT) images is critical for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Convolutional neural network (CNN)-based methods are currently dominating volumetric image segmentation, but these methods suffer from the limited GPU memory and the large image size ( e.g ., 512 × 512 × 448). Typical ad-hoc strategies, such as down-sampling or patch cropping, will degrade segmentation accuracy due to insufficient capturing of local fine details or global contextual information...
September 2021: Machine Learning in Medical Imaging
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