ORCID Profile
0000-0002-3665-9021
Current Organisation
Monash University
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Publisher: Elsevier BV
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 02-12-2022
DOI: 10.1007/S10618-022-00894-5
Abstract: Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains have demonstrated that with the availability of massive amounts of time series, ML and DL techniques are competitive in time series forecasting. Nevertheless, the different forms of non-stationarities associated with time series challenge the capabilities of data-driven ML models. Furthermore, due to the domain of forecasting being fostered mainly by statisticians and econometricians over the years, the concepts related to forecast evaluation are not the mainstream knowledge among ML researchers. We demonstrate in our work that as a consequence, ML researchers oftentimes adopt flawed evaluation practices which results in spurious conclusions suggesting methods that are not competitive in reality to be seemingly competitive. Therefore, in this work we provide a tutorial-like compilation of the details associated with forecast evaluation. This way, we intend to impart the information associated with forecast evaluation to fit the context of ML, as means of bridging the knowledge gap between traditional methods of forecasting and adopting current state-of-the-art ML techniques.We elaborate the details of the different problematic characteristics of time series such as non-normality and non-stationarities and how they are associated with common pitfalls in forecast evaluation. Best practices in forecast evaluation are outlined with respect to the different steps such as data partitioning, error calculation, statistical testing, and others. Further guidelines are also provided along selecting valid and suitable error measures depending on the specific characteristics of the dataset at hand.
Publisher: Elsevier BV
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Public Library of Science (PLoS)
Date: 27-12-2017
Publisher: SPIE
Date: 04-03-2010
DOI: 10.1117/12.845587
Publisher: Springer Science and Business Media LLC
Date: 08-08-2017
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 04-2023
Publisher: Public Library of Science (PLoS)
Date: 30-11-2018
Publisher: Elsevier BV
Date: 04-2018
Publisher: Springer Science and Business Media LLC
Date: 04-11-2009
DOI: 10.1007/S11548-008-0258-X
Abstract: By adding a tracking sensor to a 3D ultrasound (US) probe and thus locating the probe in space, new applications within the fields of image guided surgery and radiation therapy are possible. To locate the US volume in space, a calibration is necessary to determine the mathematical transformation for mapping points from the tracking coordinate system to the US image coordinate system. We present a comprehensive comparison of two different approaches to perform this calibration for 3D US. For both approaches a phantom is scanned and located in the images by means of segmentation and registration techniques. Calibration is then performed by either relating the tracked phantom's (TP) spatial location to the calibration scans, or by solely correlating scans taken from multiple perspectives when using hand-eye calibration methods (HE). Depending on which approach is utilized, a minimum of one or three images, respectively, need to be acquired for the calibration process. We evaluated both approaches for calibration and reconstruction precision. Regarding the latter, the performed tests led to mean target localization errors of 3.5 mm (HE) and 3.3 mm (TP) for real data, and of 1.4 mm (HE) and 0.9 mm (TP) for simulated data. Our results indicate that taking additional scans leads to a significant improvement in the calibration. Furthermore, the obtained calibration and reconstruction precisions suggest the use of a TP.
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 09-2012
DOI: 10.1016/J.CMPB.2011.09.017
Abstract: In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: IEEE
Date: 11-2011
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 05-2012
Publisher: Elsevier BV
Date: 08-2022
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 07-2022
Publisher: Georg Thieme Verlag KG
Date: 22-05-2013
Abstract: The aims of this study were: to compare energy expenditure (EE) estimated from the existing GT3X accelerometer equations and EE measured with indirect calorimetry to define new equations for EE estimation with the GT3X in youth, adults and older people and to define GT3X vector magnitude (VM) cut points allowing to classify PA intensity in the aforementioned age-groups. The study comprised 31 youth, 31 adults and 35 older people. Participants wore the GT3X (setup: 1-s epoch) over their right hip during 6 conditions of 10-min duration each: resting, treadmill walking/running at 3, 5, 7, and 9 km · h⁻¹, and repeated sit-stands (30 times · min⁻¹). The GT3X proved to be a good tool to predict EE in youth and adults (able to discriminate between the aforementioned conditions), but not in the elderly. We defined the following equations: for all age-groups combined, EE (METs)=2.7406+0.00056 · VM activity counts (counts · min⁻¹)-0.008542 · age (years)-0.01380 · body mass (kg) for youth, METs=1.546618+0.000658 · VM activity counts (counts · min⁻¹) for adults, METs=2.8323+0.00054 · VM activity counts (counts · min⁻¹)-0.059123 · body mass (kg)+1.4410 · gender (women=1, men=2) and for the elderly, METs=2.5878+0.00047 · VM activity counts (counts · min⁻¹)-0.6453 · gender (women=1, men=2). Activity counts derived from the VM yielded a more accurate EE estimation than those derived from the Y-axis. The GT3X represents a step forward in triaxial technology estimating EE. However, age-specific equations must be used to ensure the correct use of this device.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 05-09-2019
DOI: 10.3390/JCM8091390
Abstract: Clinical audit of invasive mold disease (IMD) in hematology patients is inefficient due to the difficulties of case finding. This results in antifungal stewardship (AFS) programs preferentially reporting drug cost and consumption rather than measures that actually reflect quality of care. We used machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions and benchmarked against key AFS measures. NLP screened 3014 reports from 1 September 2008 to 31 December 2017, generating 784 positives that after review, identified 205 IMD episodes (44% probable-proven) in 185 patients from 50,303 admissions. Breakthrough-probable roven-IMD on antifungal prophylaxis accounted for 60% of episodes with serum monitoring of voriconazole or posaconazole in the 2 weeks prior performed in only 53% and 69% of episodes, respectively. Fiberoptic bronchoscopy within 2 days of CT scan occurred in only 54% of episodes. The average turnaround of send-away bronchoalveolar galactomannan of 12 days (range 7–22) was associated with high empiric liposomal hotericin consumption. A random audit of 10% negative reports revealed two clinically significant misses (0.9%, 2/223). This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. Compared to current methods of clinical audit, semi-automated surveillance using NLP is more efficient and inclusive by avoiding restrictions based on any underlying hematologic condition, and has the added advantage of being potentially scalable.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Berlin Heidelberg
Date: 2012
No related grants have been discovered for Christoph Bergmeir.