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- Hoever, P., et al.
(författare)
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Orexin Receptor Antagonism, a New Sleep-Enabling Paradigm: A Proof-of-Concept Clinical Trial
- 2012
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Ingår i: Clinical pharmacology and therapeutics. - : Springer Science and Business Media LLC. - 1532-6535 .- 0009-9236. ; 91:6, s. 975-985
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Tidskriftsartikel (refereegranskat)abstract
- The orexin system is a key regulator of sleep and wakefulness. In a multicenter, double-blind, randomized, placebo-controlled, two-way crossover study, 161 primary insomnia patients received either the dual orexin receptor antagonist almorexant, at 400, 200, 100, or 50 mg in consecutive stages, or placebo on treatment nights at 1-week intervals. The primary end point was sleep efficiency (SE) measured by polysomnography; secondary end points were objective latency to persistent sleep (LPS), wake after sleep onset (WASO), safety, and tolerability. Dose-dependent almorexant effects were observed on SE, LPS, and WASO. SE improved significantly after almorexant 400 mg vs. placebo (mean treatment effect 14.4%; P < 0.001). LPS (-18 min (P = 0.02)) and WASO (-54 min (P < 0.001)) decreased significantly at 400 mg vs. placebo. Adverse-event incidence was dose-related. Almorexant consistently and dose-dependently improved sleep variables. The orexin system may offer a new treatment approach for primary insomnia.
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| 2. |
- Mahbod, A., et al.
(författare)
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Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
- 2020
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 193, s. 105475-
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Tidskriftsartikel (refereegranskat)abstract
- Background and objective: Skin cancer is among the most common cancer types in the white population and consequently computer aided methods for skin lesion classification based on dermoscopic images are of great interest. A promising approach for this uses transfer learning to adapt pre-trained convolutional neural networks (CNNs) for skin lesion diagnosis. Since pre-training commonly occurs with natural images of a fixed image resolution and these training images are usually significantly smaller than dermoscopic images, downsampling or cropping of skin lesion images is required. This however may result in a loss of useful medical information, while the ideal resizing or cropping factor of dermoscopic images for the fine-tuning process remains unknown. Methods: We investigate the effect of image size for skin lesion classification based on pre-trained CNNs and transfer learning. Dermoscopic images from the International Skin Imaging Collaboration (ISIC) skin lesion classification challenge datasets are either resized to or cropped at six different sizes ranging from 224 × 224 to 450 × 450. The resulting classification performance of three well established CNNs, namely EfficientNetB0, EfficientNetB1 and SeReNeXt-50 is explored. We also propose and evaluate a multi-scale multi-CNN (MSM-CNN) fusion approach based on a three-level ensemble strategy that utilises the three network architectures trained on cropped dermoscopic images of various scales. Results: Our results show that image cropping is a better strategy compared to image resizing delivering superior classification performance at all explored image scales. Moreover, fusing the results of all three fine-tuned networks using cropped images at all six scales in the proposed MSM-CNN approach boosts the classification performance compared to a single network or a single image scale. On the ISIC 2018 skin lesion classification challenge test set, our MSM-CNN algorithm yields a balanced multi-class accuracy of 86.2% making it the currently second ranked algorithm on the live leaderboard. Conclusions: We confirm that the image size has an effect on skin lesion classification performance when employing transfer learning of CNNs. We also show that image cropping results in better performance compared to image resizing. Finally, a straightforward ensembling approach that fuses the results from images cropped at six scales and three fine-tuned CNNs is shown to lead to the best classification performance.
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