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During this time of increased COVID activity in our area, please minimize in-person visits to the hospital for Medical Records and Patient Accounts purposes. Houlton Regional Hospital offers multiple ways to assist with these needs.

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For Medical Records, call ahead and ask for copies of your records to be mailed to you, or request a quick pick up at the Main Entrance. The Medical Records Department can be reached at 207-532-2900, x2212.

At this time we are still asking for everyone to call ahead to request Medical Record Forms. Please call 521-2212. Arrangements will be made for pick up, or they can be mailed or faxed. Thank you in advance for your cooperation!

As Caregivers at Houlton Regional Hospital continue to be prepared for COVID-19 treatment and screening, our steadfast commitment to safely caring for our Patients, our Staff, and our Medical Staff is our top priority. Houlton Regional Hospital has formed a specialized Pandemic (COVID-19) Response Team, consisting of Infection Control, Emergency Care, Hospital Leadership, Nursing Leadership, Medical Staff Leadership, Facilities, Safety and Security that has been working closely with the Maine CDC and is prepared for any potential case of COVID-19 presenting at the hospital. At present, we ask that each person coming to Houlton Regional Hospital please put on a protective mask prior to entering the hospital.

If you have been in contact with someone with COVID-19 or if you have recently (within 14 days) been in a community where there is ongoing spread of COVID-19 and develop symptoms of COVID-19, call your healthcare provider and tell them about your symptoms and your exposure. They will decide whether you need to be tested, but keep in mind that there is no treatment for COVID-19 and people who are mildly ill may be able to isolate and care for themselves at home.

AbstractThe ability to direct visual attention is a fundamental skill for seeing robots. Attention comes in two flavours: the gaze direction (overt attention) and attention to a specific part of the current field of view (covert attention), of which the latter is the focus of the present study. Specifically, we study the effects of attentional masking within pre-trained deep neural networks for the purpose of handling ambiguous scenes containing multiple objects. We investigate several variants of attentional masking on partially pre-trained deep neural networks and evaluate the effects on classification performance and sensitivity to attention mask errors in multi-object scenes. We find that a combined scheme consisting of multi-level masking and blending provides the best trade-off between classification accuracy and insensitivity to masking errors. This proposed approach is denoted multilayer continuous-valued convolutional feature masking (MC-CFM). For reasonably accurate masks it can suppress the influence of distracting objects and reach comparable classification performance to unmasked recognition in cases without distractors. 041b061a72