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Knee Cartilage Segmentation Using Diffusion-Weighted MRI
[article]
2019
arXiv
pre-print
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The integrity of articular cartilage is a crucial aspect in the early diagnosis of osteoarthritis (OA). Many novel MRI techniques have the potential to assess compositional changes of the cartilage extracellular matrix. Among these techniques, diffusion tensor imaging (DTI) of cartilage provides a simultaneous assessment of the two principal components of the solid matrix: collagen structure and proteoglycan concentration. DTI, as for any other compositional MRI technique, require a human
arXiv:1912.01838v1
fatcat:dlxmvvsx5rfzjaqy3dg76pojlm
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... to perform segmentation manually. The manual segmentation is error-prone and time-consuming (∼ few hours per subject). We use an ensemble of modified U-Nets to automate this segmentation task. We benchmark our model against a human expert test-retest segmentation and conclude that our model is superior for Patellar and Tibial cartilage using dice score as the comparison metric. In the end, we do a perturbation analysis to understand the sensitivity of our model to the different components of our input. We also provide confidence maps for the predictions so that radiologists can tweak the model predictions as required. The model has been deployed in practice. In conclusion, cartilage segmentation on DW-MRI images with modified U-Nets achieves accuracy that outperforms the human segmenter. Code is available at https://github.com/aakashrkaku/knee-cartilage-segmentation
Deep Probability Estimation
[article]
2022
arXiv
pre-print
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Reliable probability estimation is of crucial importance in many real-world applications where there is inherent uncertainty, such as weather forecasting, medical prognosis, or collision avoidance in autonomous vehicles. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a patient has died or not), because the ground-truth probabilities of the events of interest are typically unknown. The problem is therefore analogous to binary
arXiv:2111.10734v3
fatcat:rhvzwpjz6zartky5z3vswmaj3q
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... cation, with the important difference that the objective is to estimate probabilities rather than predicting the specific outcome. The goal of this work is to investigate probability estimation from high-dimensional data using deep neural networks. There exist several methods to improve the probabilities generated by these models but they mostly focus on classification problems where the probabilities are related to model uncertainty. In the case of problems with inherent uncertainty, it is challenging to evaluate performance without access to ground-truth probabilities. To address this, we build a synthetic dataset to study and compare different computable metrics. We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty. We also give a theoretical analysis of a model for high-dimensional probability estimation which reproduces several of the phenomena evinced in our experiments. Finally, we propose a new method for probability estimation using neural networks, which modifies the training process to promote output probabilities that are consistent with empirical probabilities computed from the data. The method outperforms existing approaches on most metrics on the simulated as well as real-world data.
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
[article]
2021
Zenodo
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Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetitions. The optimal training content and quantity are currently unknown because no practical tools exist to measure them. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning
doi:10.5281/zenodo.5801898
fatcat:djaesx6bqvewfcofcjd4ucpbze
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... odel to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into component functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that these advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
[article]
2021
arXiv
pre-print
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More recently, convolutional neural networks (Wen & Keyes, 2019; Kaku et al., 2020) and recurrent neural networks (Singh et al., 2016) have been applied to this problem Inspired by the success of temporal ...
arXiv:2111.02521v1
fatcat:iacxc6xlsrbw3lzmwycdbd5e5y
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
[article]
2020
arXiv
pre-print
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Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of such variables can degrade the performance of deep-learning models. We study three datasets where there is a strong influence of known extraneous variables: classification of upper-body movements in stroke patients, annotation of surgical activities, and recognition of corrupted images. Models trained with batch
arXiv:2002.04019v2
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... tion learn features that are highly dependent on the extraneous variables. In batch normalization, the statistics used to normalize the features are learned from the training set and fixed at test time, which produces a mismatch in the presence of varying extraneous variables. We demonstrate that estimating the feature statistics adaptively during inference, as in instance normalization, addresses this issue, producing normalized features that are more robust to changes in the extraneous variables. This results in a significant gain in performance for different network architectures and choices of feature statistics.
DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
[article]
2019
arXiv
pre-print
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Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present
arXiv:1911.05567v2
fatcat:5a2iuy53cbayhdymuxyncvg544
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... a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation). Our tool and trained models are available at https://github.com/NYUMedML/DARTS
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2112.11330v1 fatcat:uimwncxvvfbtln77fizn2guxoy
Kaku, A. et al. Sequence-to-Sequence Modeling for Action Identification at High
Temporal Resolution. ArXiv abs/2111.02521 (2021).
25. Kaku, A. et al. ...
Kaku.
8 These authors jointly supervised this work: Carlos Fernandez-Granda, Heidi Schambra. ...
arXiv:2112.11330v2
fatcat:adfljl6g5rcdtnlovztlbdljra
Towards data-driven stroke rehabilitation via wearable sensors and deep learning
[article]
2020
arXiv
pre-print
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In the case of CNNs, recent work by Kaku et al. (2020) suggests that batch normalization may be particularly sensitive to such shifts. ...
However, if the distributions of the training and test data differ, then these statistics may not center and normalize the data adequately, as demonstrated by Kaku et al. (2020) . ...
arXiv:2004.08297v3
fatcat:zw7za4y7ybf4ffoxsbjqxpc4xa
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
[article]
2020
arXiv
pre-print
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During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating
arXiv:2008.01774v2
fatcat:n6oo2illcfaqhiqpiiu5cxlxfy
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... eristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
Perancangan Tata Kelola Teknologi Informasi di BAPAPSI Pemkab Bandung Menggunakan framework COBIT 5 Pada Domain EDM dan DSS
2016
Journal of Information Systems Engineering and Business Intelligence
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Lima prinsip menekankan pada tujuan dan penciptaan nilai antara para pemangku kepentingan yang berbeda yang mungkin mengharapkan nilai TI yang berbeda pula (George, Aakash, & Singh, 2014
B. ...
Pelayanan pemerintah yang birokratis dan terkesan kaku dapat diminimalisir melalui pemanfaatan e-government agar menjadi lebih fleksibel dan lebih berorientasi pada kepuasan pelanggan (Burdefira, 2013 ...
doi:10.20473/jisebi.2.2.74-80
fatcat:rqskf3mznnhuzcd2uaf52nommm
Feminism, Womanhood And Motherhood In The Works Of Jhumpa Lahiri
2016
Zenodo
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In "Hell -Heaven" Lahiri looks at the psyche of a married woman in an alien land through Aparna who falls in love with Pranab kaku, a Bengali man much younger to her. ...
Though the decision to ask her father to stay with her was in her own selfish interests, as in her father"s company she finds her son Aakash more cultured, civilized, calmer and cooler. ...
doi:10.5281/zenodo.161597
fatcat:vyvl7d2uljevpeqnyrpxis2l2i
Conflict of Interest Disclosures
2019
Global Spine Journal
Preserved Fulltext
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Guofeng
Bao, Hongda
Bao, Mike
Barbagallo, Giuseppe
Barbagli, Giovanni
Barbanti-Brodano, Giovanni
Barber, Stacey
Barbero, Andrea
Bardonova, Liudmila
Barimani, Bardia
Barkley, Sarah
Barkoh, Kaku ...
, Corinna
Abbasi, Hamid
05: AMW-research grant
Acaroglu, Emre
02: AO Spine 04: IncredX 05: DePuy Synthes Spine 06: Medtronic
Adhikari, Prashant
06: Grants/research support: Medtronic
Agarwal, Aakash ...
doi:10.1177/2192568219852642
pmid:31211026
pmcid:PMC6555103
fatcat:g7ro6e55tbcwhpstzf4dw6bvzm
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
[article]
2021
arXiv
pre-print
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Farah E Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, et al. ...
arXiv:2106.07049v2
fatcat:mxwcpxslfrbxpl2sfenr6c2vh4