Dr. Daniela Ushizima
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Academic Career Length
17Years (2010-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 March 2019 in Computerized Medical Imaging and Graphics
Journal Article
Flavio Araujo
Romuere SilvaDaniela UshizimaMRMariana T. Rezende
Mariana T. RezendeCCclaudia carneiro
claudia carneiroFMFatima Medeiros
Fatima Medeiros +1
10 June 2021 in Scientific Data
Journal Article
MRMariana T. Rezende
Mariana T. RezendeRSRaniere Silva
Raniere SilvaFBFagner de O. Bernardo
Fagner de O. BernardoATAlessandra H. G. Tobias
Alessandra H. G. TobiasPOPaulo H. C. Oliveira
Paulo H. C. OliveiraABAndrea Gomes Campos Bianchi
Andrea Gomes Campos Bianchi +5
Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.
1 June 2023 in npj Computational Materials
Journal Article
YHYing Huang
Ying Huang
DPDavid Perlmutter
David Perlmutter
ASAndrea Fei-Huei Su
Andrea Fei-Huei Su
JQJerome Quenum
Jerome Quenum
PSPavel Shevchenko
Pavel Shevchenko
Daniela Ushizima +2
Operando X-ray micro-computed tomography (µCT) provides an opportunity to observe the evolution of Li structures inside pouch cells. Segmentation is an essential step to quantitatively analyzing µCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the datasets. Herein, we report a computational approach, batteryNET, to train an Iterative Residual U-Net-based network to detect Li structures. The resulting semantic segmentation shows singular Li-related component changes, addressing diverse morphologies in the dataset. In addition, visualizations of the dead Li are provided, including calculations about the volume and effective thickness of electrodes, deposited Li, and redeposited Li. We also report discoveries about the spatial relationships between these components. The approach focuses on a method for analyzing battery performance, which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.
Journal Article
DDDébora N. Diniz
Débora N. DinizMRMariana T. Rezende
Mariana T. RezendeABAndrea Gomes Campos Bianchi
Andrea Gomes Campos BianchiCCclaudia carneiro
claudia carneiroELEduardo Luz
Eduardo Luz
Marcone Jamilson Freitas Souza +3
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
9 October 2012 in IEEE Transactions on Visualization and Computer Graphics
Journal Article
Daniela UshizimaDMDmitriy Morozov
Dmitriy Morozov
GWGunther H. Weber
Gunther H. WeberABAndrea G.C. Bianchi
Andrea G.C. Bianchi
JSJames A. Sethian
James A. Sethian
EBE. Wes Bethel
E. Wes Bethel
One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. We also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.
9 August 2021 in Scientific Reports
Journal Article
RSRobbie Sadre
Robbie Sadre
BSBaskaran Sundaram
Baskaran Sundaram
SMSharmila Majumdar
Sharmila Majumdar
Daniela UshizimaThe new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.
About Me
Dani Ushizima, Ph.D., is a SENIOR Scientist at Berkeley Lab, also Affiliated Faculty with the Bakar Institute at UC San Francisco, and Berkeley Institute for Data Science (BIDS) at UC Berkeley. She leads the Machine Learning (ML) team for the Center of Advanced Mathematics for Energy Research Applications (CAMERA) at LBNL. Since 1996, Ushizima has investigated, developed and deployed advanced algorithms to extract information from scientific data for decision making. Together with her team at Berkeley Lab, they develop computer vision software that exploits unique ML and datasets from microscopes (e.g. X-ray, electron, tomography) for quantitative analysis. She has led projects ranging from quality control of materials, plant growth to testing radioactive markers for biomedical imaging. In addition to high-resolution, high-throughput image sources, her research also includes ML such as transformers applied to natural language processing. Awarded DOE Early Career fellowship in 2016. Development of pyCBIR, an image recommendation software. Nominated the LBNL Director's Award in 2017, followed by the LBNL Women@Lab in 2018, the Latina Scientist in 2021 and the PMWC Pioneer Award in 2023. Scientific diplomacy with the U.S. Dept. of State TechWomen. Cooperation with UC San Francisco in the design of algorithms to improve cell counting and analyses of biomedical imaging. Visit Ushizima's website for more information. http://ushizima.com