ライフサイエンスコーパス: medical imaging

1 onomous navigation, reconnaissance, and even medical imaging.

2 science, pharmaceuticals, agrochemicals, and medical imaging.

3 nces that can be visualized noninvasively by medical imaging.

4 ith potential applications in biological and medical imaging.

5 ng to applications from energy harvesting to medical imaging.

6 rstones to enhancing the radiation safety of medical imaging.

7 sonance (EPR), fluorescence spectroscopy and medical imaging.

8 h to drug screening and discovery as well as medical imaging.

9 nd cumulative annual radiation exposure from medical imaging.

10 eening, communications, quality control, and medical imaging.

11 e high exposure to ionizing radiation due to medical imaging.

12 anced interest in exposure to radiation from medical imaging.

13 in vivo dynamics of gliomas visualized with medical imaging.

14 ical sciences, it also plays a vital role in medical imaging.

15 ethods used in humans provides comprehensive medical imaging.

16 otechnology, drug discovery, and potentially medical imaging.

17 r in actual NIR contrast-enhanced diagnostic medical imaging.

18 ality and information processing devices, or medical imaging.

19 relates tissue neuropathological analysis to medical imaging.

20 eywords related to psoriasis, synovitis, and medical imaging.

21 s, we focus our discussion on biological and medical imaging.

22 learning (AI-ML) have taken center stage in medical imaging.

23 and improve technologies within the field of medical imaging.

24 ient-specific predictions when combined with medical imaging.

25 in applications ranging from LED displays to medical imaging.

26 enhance the robustness of AI applications in medical imaging.

27 vely automate complex and laborious tasks in medical imaging.

28 he cornerstone of artificial intelligence in medical imaging.

29 ding diagnostic and therapeutic decisions in medical imaging.

30 ted with existing soft robotic platforms and medical imaging.

31 k purpose, making them difficult to apply in medical imaging.

32 elligence (AI), with growing applications in medical imaging.

33 ures may quantify characteristics present in medical imaging.

34 l to improve environmental sustainability in medical imaging.

35 continues to garner substantial interest in medical imaging.

36 age, sex, diagnosed obesity, and the use of medical imaging.

37 e molecular to the organism scale, excluding medical imaging.

38 cessful commercialization in synchrotron and medical imaging.

39 s data derived from clinical information and medical imaging.

40 understanding of anatomy to be derived from medical imaging.

41 n drug discovery and development, as well as medical imaging.

42 I model creation, and identify innovators in medical imaging.

43 nd clinical use in medicine and the field of medical imaging.

44 e with or even below those currently used in medical imaging.

45 f multiple myeloma is frequently observed in medical imaging.

46 ces, optical computing, and in vivo infrared medical imaging.

47 uctive materials, sensors, drug delivery and medical imaging.

48 road relevance to many other applications in medical imaging.

49 ically improve soft tissue contrast in X-ray medical imaging.

50 nizing radiation potentially associated with medical imaging (2).

51 sting topic in structural health monitoring, medical imaging, aerospace and nuclear instrumentation.

52 entities for site-specific drug delivery and medical imaging after parenteral administration.

53 ntific community about ways to create useful medical imaging AI in a future pandemic.

54 However, of the several hundred medical imaging AI models developed for COVID-19, very f

55 y evaluated the feasibility of the Perimeter Medical Imaging AI Otis WF-OCT device at a single academ

56 ectronic sensors, compared with conventional medical imaging, allow monitoring of advanced physiologi

57 c media, resulting in anxiety and fear about medical imaging among patients and parents.

58 In microarray analysis, medical imaging analysis and functional magnetic resonan

59 a thorough review of existing XAI methods in medical imaging analysis with evaluation availability.

60 ally evaluate the quality of explanations in medical imaging analysis.

61 age processing applications, particularly in medical imaging and AI-based image classification.

62 ensitivity that is otherwise challenging for medical imaging and blood biomarkers to achieve.

63 is playing an increasingly important role in medical imaging and can help solve many of the challenge

64 in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) mode

65 integrates interdisciplinary expertise from medical imaging and engineering.

66 ests applications of hyperpolarized 15N2O in medical imaging and for flow and diffusion studies.

67 cular imaging modality more centrally within medical imaging and for the integration of nuclear medic

68 The medical imaging and image processing techniques, ranging

69 Because ionizing radiation is widely used in medical imaging and in military, industry, and commercia

70 ing data sharing policies, and standardizing medical imaging and in vitro diagnostics.

71 Fractures included were confirmed by medical imaging and included both traumatic and minimal

72 Despite ever-increasing use of medical imaging and laboratory tests intended to promote

73 uding deep learning (DL) methods for routine medical imaging and large language models (LLMs) for ele

74 Advanced techniques, including medical imaging and machine learning, are widely used fo

75 Possible applications include medical imaging and material evaluation.

76 approach appears particularly beneficial for medical imaging and nondestructive testing.

77 neral blood test, colonoscopy, colon biopsy, medical imaging and stool testing.

78 niques are rapidly emerging in the fields of medical imaging and targeted drug delivery.

79 olar energy conversion, fuel-cell catalysis, medical imaging and therapy, and electronics.

80 unctional nanomaterials with applications in medical imaging and therapy.

81 s such as particle detection, dosimetry, and medical imaging and therapy.

82 Medical imaging and viewing software were used to compar

83 n varied applications such as drug delivery, medical imaging, and advanced materials, as well as in f

84 n specific application domains, particularly medical imaging, and consequently leading to overfitting

85 drivers of costs: end-of-life care patterns, medical imaging, and drugs.

86 ated to nonproliferation, homeland security, medical imaging, and gamma-ray telescopes.

87 vices, with applications in inkjet printing, medical imaging, and synthesis of particulate materials.

88 ussion of computer vision focuses largely on medical imaging, and we describe the application of natu

89 I) have achieved expert-level performance in medical imaging applications.

90 asingly achieved expert-level performance in medical imaging applications.

91 s have brought remarkable success in various medical imaging applications.

92 As drug delivery, therapy, and medical imaging are becoming increasingly cell-specific,

93 predict human performance are of interest in medical imaging as substitutes in psychophysical studies

94 n over the impact of radiation exposure from medical imaging, as well as on the cost of diagnostic me

95 t category "radiology, nuclear medicine, and medical imaging" at the Institute of Science Information

96 enabled tracking of respiratory transport in medical imaging-based anatomic domains shows that the re

97 an important component of data curation for medical imaging-based artificial intelligence model deve

98 respiratory airflow and droplet transport in medical imaging-based digital models.

99 imaging has great potential in the field of medical imaging because it offers several major advantag

100 S: Retrospective cohort study of patterns of medical imaging between 2000 and 2016 among 16 million t

101 ly related to Definity (Bristol-Myers Squibb Medical Imaging, Billerica, Massachusetts) administratio

102 ntrast agents Definity (Bristol-Myers Squibb Medical Imaging, Billerica, Massachusetts) and Optison (

103 ved intravenous contrast (Definity, Lantheus Medical Imaging, Billerica, Massachusetts).

104 g approach identified known as well as novel medical imaging biomarkers without any prior domain know

105 Here, by combining medical imaging, biomechanical data, live behavioral exp

106 copic lesions are frequently detected during medical imaging, but it is unclear how they form or prog

107 ntelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or rep

108 arch group has now ported this technology to medical imaging by designing a whole-body FFC Magnetic R

109 adoption of artificial intelligence (AI) for medical imaging by resource-poor health institutions.

110 nce has recently made a disruptive impact in medical imaging by successfully automatizing expert-leve

111 As a noninvasive technique, medical imaging can be performed at low risk and inconve

112 Observations: Medical imaging can provide a comprehensive macroscopic

113 Medical imaging can visualize characteristics of human c

114 For example, in the 2024 Medical Imaging Challenge Dataset, the Residual Attentio

115 In medical imaging, challenges are competitions that aim to

116 rgery, radiation oncology, medical oncology, medical imaging, clinical pathology and lab medicine, so

117 Accordingly, the medical imaging community must ensure that the benefits

118 characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithm

119 mulations, including drug delivery vehicles, medical imaging contrast agents, and integral membrane p

120 Analysis of 3D medical imaging data has been a large topic of focus in

121 ors describe fundamental steps for preparing medical imaging data in AI algorithm development, explai

122 Labeled medical imaging data is scarce and expensive to generate

123 fficient, and reproducible interpretation of medical imaging data to improve patient care in paediatr

124 BrainFlow incorporates patient-specific medical imaging data, pulsatile flow to mimic cardiac cy

125 wdsourced competition using an international medical imaging dataset is provided.

126 earch settings, the scarcity of high-quality medical imaging datasets has hampered the potential of a

127 ere are relatively few diverse, high-quality medical imaging datasets on which to train computer visi

128 light on the importance of gender balance in medical imaging datasets used to train AI systems for co

129 s are summarized across three key areas: (a) medical imaging datasets, (b) demographic definitions, a

130 In most cases, the development of a medical imaging device was opportunistic; many scientist

131 logical and materials research, and portable medical imaging devices, and would substantially reduce

132 We suggest that the FDA Medical Imaging Division convene a panel of cardiologist

133 Medical imaging does not provide diagnosis of EVD, but p

134 for many biomedical applications, including medical imaging, drug delivery, and antimicrobial coatin

135 ubiquity of DNA sequencing and the advent of medical imaging, electronic health records, and "omics"

136 Several radionuclides used in medical imaging emit Auger electrons, which, depending o

137 Given the widespread use of medical imaging, evaluating the effectiveness of interve

138 Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted

139 Patients who undergo frequent medical imaging examinations can accumulate doses that a

140 In some cases, medical imaging examinations may be delayed or deferred

141 Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic ch

142 e algorithms, several limitations persist in medical imaging fields, where a lack of data is a common

143 ar approaches may be worthwhile across other medical imaging fields.

144 nal oncology, as research and development in medical imaging focuses on interventional needs, it is l

145 nd porous media, with applications including medical imaging, food characterization and oil-well logg

146 Deployment of AI systems using medical imaging for disease diagnosis with such biases r

147 In this review, the current status of medical imaging for intervention in oncology will be des

148 to the incredible opportunities provided by medical imaging for preoperative planning, intraoperativ

149 ussed on the basis of a typical work flow in medical imaging, grouped by planning, scanning, interpre

150 gned to coordinate metal ions or chelates to medical imaging has allowed for significant breakthrough

151 ficial intelligence (AI), its application in medical imaging has been burdened and limited by expert-

152 Traditionally, medical imaging has defined anatomy, but increasingly ne

153 Medical imaging has enormous potential for early disease

154 PURPOSE OF REVIEW: Radiation exposure due to medical imaging has grown exponentially over the past tw

155 zation in imaging: the low-dose radiation of medical imaging has no documented pathway to harm, where

156 Over the past decade, medical imaging has played an increasingly valuable role

157 Medical imaging has seen substantial and rapid technical

158 ems that use artificial intelligence (AI) in medical imaging have been developed, such as the interpr

159 in developmental biology, neuroscience, and medical imaging have brought us closer than ever to unde

160 Advances in cancer biology and medical imaging have led to a number of cancer screening

161 g and high light yield have been created for medical imaging, high energy physics and national securi

162 or allied health services (ICC = 18 to 26%), medical imaging (ICC = 4 to 10%), and the ICU (ICC = 5 t

163 of radiation-induced hematologic cancer from medical imaging in children and adolescents might suppor

164 nificant implications related to the role of medical imaging in didactic and research.

165 espite numerous applications in oncology and medical imaging in general, there is no consensus regard

166 ntional needs, it is likely that the role of medical imaging in intervention will become even more in

167 Medical imaging in interventional oncology is used diffe

168 ligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acq

169 s disease entity; and to address the role of medical imaging in this patient population.

170 ntrast agent (Definity, Bristol-Myers Squibb Medical Imaging Inc., North Billerica, Massachusetts) wa

171 t agents (35% Definity, Bristol Myers Squibb Medical Imaging Inc., North Billerica, Massachusetts; 65

172 The use of ionizing radiation in medical imaging, including CT, provides valuable diagnos

173 Medical imaging increased rapidly from 2000 to 2006, but

174 Excessive use of medical imaging increases health care costs and exposure

175 es, private payers, government agencies, the medical imaging industry, and experts in quality measure

176 Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Rad

177 s due to limited access to and high costs of medical imaging infrastructure.

178 tions have been shown to be accelerators for medical imaging innovations, but their impact is hindere

179 record (EHR) to provide a context for their medical imaging interpretation.

180 clinicians rely on EMR to provide context in medical imaging interpretation.

181 impact of employing real-time automation of medical imaging into the care of the critically ill.

182 Contemporary medical imaging is becoming increasingly more quantitati

183 Medical imaging is crucial for diagnosis, phenotyping, a

184 Medical imaging is entering the new millennium with a so

185 Medical imaging is essential to screening, early diagnos

186 nosis and management, access to high-quality medical imaging is necessary.

187 made in limited-resource environments where medical imaging is needed.

188 Medical imaging is routine in the diagnosis and staging

189 uantification of the tumor phenotype through medical imaging, is a promising development for precisio

190 apidly approaches human-level performance in medical imaging, it is crucial that it does not exacerba

191 In scientific studies with medical imaging, it is important that the process involv

192 nd collected from various sources, including medical imaging, laboratory tests and genome sequencing.

193 ons are vast and include the entirety of the medical imaging life cycle from image creation to diagno

194 esented in the top 100 cited articles in the medical imaging literature.

195 Hence, we are pioneering a new medical imaging method, called Magnetic Particle Imaging

196 ge electromechanical coupling for ultrasound medical imaging, microfluidic control, mechanical sensin

197 violet (UV) spectrum is broadly important to medical imaging, military target tracking, remote sensin

198 ing analysis from photography and all common medical imaging modalities and present an alternative to

199 e tools used to investigate the AVA, such as medical imaging modalities, experimental methods, and co

200 observable with the millimeter-resolution of medical imaging modalities.

201 , demonstrating its effectiveness in diverse medical imaging modalities.

202 itous challenge, crucial in radio astronomy, medical imaging, navigation, and classical and quantum c

203 oad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology, and Psycholog

204 anipulation, which can be further applied to medical imaging, nondestructive testing, and acoustic co

205 ses during an intravenous Definity (Lantheus Medical Imaging, North Billerica, Massachusetts) infusio

206 The growth in medical imaging over the past 2 decades has yielded unar

207 Despite the advances made in medical imaging over the past 3 decades and the central

208 I (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverag

209 Medical imaging plays a fundamental role in oncology and

210 In general, medical imaging plays five key roles in image-guided the

211 ial characterization, biological sensing and medical imaging, practical development of these applicat

212 owed how a coordinated approach to solving a medical imaging problem can be successfully conducted.

213 spurred much interest in its application to medical imaging problems.

214 rolled trials including adults who underwent medical imaging procedures assessing current health stat

215 conducted; all patients in waiting rooms for medical imaging procedures before undergoing imaging exa

216 ncy with which patients underwent diagnostic medical imaging procedures during episodes of care was c

217 ncy with which patients underwent diagnostic medical imaging procedures during episodes of outpatient

218 York, New York, that evaluated all preceding medical imaging procedures involving ionizing radiation

219 hs per year in the U.S. population caused by medical imaging procedures that use ionizing radiation.

220 Chest CT scans are one of the most common medical imaging procedures.

221 roups suggests that experience in diagnostic medical imaging produces perceptual skills that that tra

222 ing (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI mo

223 In these systems, medical imaging protection are important not only for cl

224 The yearly rate of medical imaging radiation exposure may seem small at app

225 ge and key questions in regard to sources of medical imaging radiation exposure, radiation risk estim

226 owever, concerns about ionizing radiation in medical imaging remain and can affect patient care.

227 Application of these methods to medical imaging requires further assessment and validati

228 ected to provide a powerful resource for the medical imaging research community.

229 During the past 25 years, medical imaging research has progressed in both scope an

230 markers and has potential in high-throughput medical imaging research.

231 We believe this method will be valuable to medical imaging researchers to reduce manual segmentatio

232 ques in analytical chemistry and noninvasive medical imaging, respectively.

233 the major challenges in machine learning for medical imaging: scarcity of high-quality annotated data

234 current frontier of research in the field of medical imaging science.

235 range of hybrid device applications, such as medical imaging, self-energy-harvesting touch screens an

236 and its rate of change during the study with medical imaging service use and imaging-related costs we

237 midlife to old age showed greater use of all medical imaging services, independent of FI at baseline

238 m, used previously in picture processing and medical imaging, SIFT supplements data at nonuniform poi

239 f luminescent silica-based nanoparticles for medical imaging, starting with an overview of the most c

240 Medical imaging studies achieve a diagnostic purpose and

241 are useful for the selection of variables in medical imaging studies.

242 using deep learning to advance discovery in medical imaging studies.

243 valuated a novel combined x-ray CT and SPECT medical imaging system for quantitative in vivo measurem

244 original scheme was designed to evaluate new medical imaging systems but is less successful when appl

245 Analysis was performed with QMASS (Medis Medical Imaging Systems, Leiden, the Netherlands) and HA

246 using QAngio XA 3D (Research Edition, Medis medical imaging systems, Leiden, The Netherlands).

247 nces in many imaging systems, in particular, medical imaging systems.

248 al training, they can be adapted to specific medical imaging tasks using smaller labeled datasets thr

249 ctively to other challenging cross-sectional medical imaging tasks when training data is limited.

250 Artificial intelligence (AI) models for medical imaging tasks, such as classification or segment

251 e widespread adoption of 3D U-Net in various medical imaging tasks, this critical question remains un

252 demonstrated strong potential in automating medical imaging tasks, with potential applications acros

253 ategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distri

254 Computed tomography is a widely used medical imaging technique that has high spatial and temp

255 Contrast-enhanced ultrasound (CEUS) is a medical imaging technique that offers multiple advantage

256 resonance spectroscopy (1HMRS), an emerging medical imaging technique.

257 Despite significant advances in medical imaging techniques and their routine preoperativ

258 ons share a vision to develop radiologic and medical imaging techniques through advanced quantitative

259 ies share a vision to develop radiologic and medical imaging techniques through advanced quantitative

260 ompatibility with visual inspection and with medical imaging techniques used in the NICU.

261 afficking of therapeutic cells in vivo using medical imaging techniques-known as in vivo cell trackin

262 ered an impressive variety of biosensing and medical imaging techniques.

263 Current medical imaging technologies allow visualization of tiss

264 We demonstrate the utility of nuclear medical imaging technologies and a readily available rad

265 Today's medical imaging technologies are expected to furnish ana

266 Medical imaging technologies have undergone explosive gr

267 Advances in medical imaging technologies now allow noninvasive image

268 Clinical science and medical imaging technology are traditionally displayed i

269 The outcomes of a 2011 Medical Imaging & Technology Alliance (MITA) conference

270 Recently, the Medical Imaging & Technology Alliance (MITA), a Washingt

271 ical energy (and vice versa), are crucial in medical imaging, telecommunication and ultrasonic device

272 nt practices for providing information about medical imaging tests that involve the use of radiation.

273 cedures versus other greenhouse gas-emitting medical imaging tests, as well as radiopharmaceutical wa

274 urther diminishing the amount of appropriate medical imaging that can be safely performed.

275 remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent withi

276 ms to quantify phenotypic characteristics on medical imaging through the use of automated algorithms.

277 edical fields ranging from drug delivery and medical imaging to management of vascular diseases and d

278 tectors have broad applications ranging from medical imaging to security, non-proliferation, high-ene

279 lications from displays, solar cells and bio-medical imaging to single-electron devices.

280 a wide range of potential applications, from medical imaging to surveillance.

281 ontrast agents for safe, reliable ultrasound medical imaging, tracers for magnetic resonance imaging,

282 enges and obstacles of training a very large medical imaging transformer, including data needs, biase

283 Historically used for medical imaging, ultrasound (US) has recently been shown

284 Medical imaging using SERS nanoprobes can yield higher s

285 f climate-related environmental exposures on medical imaging utilization is currently unknown.

286 The potential of this source for medical imaging was demonstrated by performing micro-com

287 Gone are the days when medical imaging was used primarily to visualize anatomic

288 sing external magnetic fields, visualized by medical imaging, we can recover tissue properties precis

289 In medical imaging, where input data may differ in size and

290 ng demonstrates a novel application of AI to medical imaging, whereby subtle regularities between dif

291 required in Poland only for those methods of medical imaging which involve the use of ionizing radiat

292 Investing in medical imaging will also be necessary to achieve substa

293 payers to discuss the key drivers of the way medical imaging will develop over the next 10 years.

294 sonation are within the physical therapy and medical imaging windows; thus the applied ultrasound is

295 sed chelator in positron emission tomography medical imaging with (64)Cu, has been synthesized using

296 Medical imaging with multimodality and whole-body techno

297 eview focuses on nanoparticles used in human medical imaging, with an emphasis on radionuclide imagin

298 oth controllers and a commercially available medical imaging workstation.

299 e model from OpenAI, has shown potential for medical imaging, yet a quantitative analysis is lacking.

300 intelligence (AI) is increasingly applied in medical imaging, yet its ability to predict biological s