KA signs Templeton as chief medical officer

AuntMinnie.com staff writers

Mar 8, 2021

2020 04 22 17 11 4900 Business Handshake Man 400

Digital x-ray manufacturer KA Imaging has tapped Dr. Philip Templeton as its chief medical officer.

A board-certified radiologist, Templeton comes to KA after serving as co-founder and chief medical officer of DocPanel Technologies. He brings over 38 years of academic and entrepreneurial experience to the newly created position, according to KA.

In other KA executive news, the firm said it also recently hired industry veterans Shawn Campbell and Robert Moccia. Campbell has been appointed as vice president of operations, while Robert Moccia has been named vice president of sales for the U.S. and Canada, according to the company.

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Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

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Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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