Mr. Luis C. Guayacán Profile

Luis C. Guayacán

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Proceedings Article | 7 April 2023 Poster + Paper

Fast detection and localization of mitosis using a semi-supervised deep representation

Santiago Castro, David Romo-Bucheli, Luis Guayacán, Fabio Martínez

Proceedings Volume 12471, 124711O (2023) https://doi.org/10.1117/12.2654516

KEYWORDS: Image processing, Sampling rates, Deep learning, Cancer, Visualization, Visual inspection, Medical image processing, Object detection

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Detection, characterization and counting of mitosis is a main biomarker in cancer, allowing diagnosis, histological grading and prognosis. Nonetheless, mitosis identification remains as a challenging task (inter-observer variability up to 20%). Even, the computational support strategies remain limited to include wide visual variability of mitotic patterns, with an inherent bias because labeled observations. This work introduces a semi-supervised scheme that learns to identify mitotic cells from an initial limited amount of labeled data. Then, the initial trained backbone is used to propagate pseudo-labels into training samples. The most challenging samples, i.e., false positive and false negative pseudo-labeled samples, are included in further batches to re-train the model. At each iteration, a set of complementary non-mitotic patches are generated from an auxiliary net. The proposed approach was validated with the public ICPR dataset, achieving competitive results of 0.74 accuracy and 0.78 sensitivity. In addition, the proposed approach achieves an average inference time of 5.21 seconds (on a batch of 240 candidate patches).

Proceedings Article | 21 December 2018 Paper

Parkinsonian gait characterization from regional kinematic trajectories

Luis Guayacán, Brayan Valenzuela, Fabio Martinez

Proceedings Volume 10975, 1097502 (2018) https://doi.org/10.1117/12.2511589

KEYWORDS: Gait analysis, Kinematics, Video, Image segmentation, Binary data, Motion analysis, Digital filtering, Head, Image classification, Motion models

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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by a set of progressive motor disabilities knows as shuffling gait patterns. The diagnosis and treatment of parkinsonian patients at different stages is typically supported by a Kinematic analysis. In clinical routine, such analysis is related with the quantitative and qualitative description of body segment displacements, computed from a reduced set of markers. Nevertheless, classical markers-based analysis has strong limitations to capture local and regional dynamic relationships associated with shuffling gait patterns. Particularly, the sparse set of markers lost sensitivity to detect progression of disease and commonly this kinematic characterization is restricted only to advanced stages. This work introduces a new hierarchical parkinsonian gait descriptor that coded kinematics at local and regional levels. At local level, a Spatial Kinematic Pattern (SKP) is computed as circular binary occurrence vectors, along trajectories. Regionally, such local vectors are grouped to describe body segments motions. Each of these regions coarsely correspond to the head, trunk and limbs. From each independent region is possible to describe kinematic patterns associated with the disease. The proposed approach was validated into a classification scheme to differentiate among regional parkinsonian patterns w.r.t to control patterns. Hence, each coding region descriptor was mapped to a support vector machine model. The proposed method was evaluated from a set of 84 gait videos of control and parkinsonian patients, achieving an average accuracy of 84, 52%.

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