Action Units Classification Using ClusWiSARD

Filho, Leopoldo Lusquino; Guarisa, Gabriel P.; Oliveira, Luiz; Filho, Aluizio Lima; França, Felipe M. G.; Lima, Priscila M. V.

doi:10.1007/978-3-030-30508-6_33

Cited by 1 publication

(1 citation statement)

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“…As mentioned, the classic WiSARD model is associated with classification problems. An extension of this model known as Regression-WiSARD (ReW) was presented in Lusquino et al 39 so that the RAM-structure nodes could be applied to problems that require the prediction of a continuous value. The main difference between the two models are i) ReW has only one set of RAM nodes and ii) each binary address in the RAM nodes stores another value, which is the sum of the expected values for the patterns presented that access the binary address.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy

Oliveira,

França,

Pereira

2023

Ultrason Imaging

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Thermal treatments that use ultrasound devices as a tool have as a key point the temperature control to be applied in a specific region of the patient’s body. This kind of procedure requires caution because the wrong regulation can either limit the treatment or aggravate an existing injury. Therefore, determining the temperature in a region of interest in real-time is a subject of high interest. Although this is still an open problem, in the field of ultrasound analysis, the use of machine learning as a tool for both imaging and automated diagnostics are application trends. In this work, a data-driven approach is proposed to address the problem of estimating the temperature in regions of a B-mode ultrasound image as a supervised learning problem. The proposal consists in presenting a novel data modeling for the problem that includes information retrieved from conventional B-mode ultrasound images and a parametric image built based on changes in backscattered energy (CBE). Then, we compare the performance of classic models in the literature. The computational results presented that, in a simulated scenario, the proposed approach that a Gradient Boosting model would be able to estimate the temperature with a mean absolute error of around 0.5°C, which is acceptable in practical environments both in physiotherapic treatments and high intensity focused ultrasound (HIFU).

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Section: Machine Learning Modelsmentioning

confidence: 99%