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Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image...
Where the larger the value of \(\alpha \) is, the larger the Loss Value contributed by the object to be segmented will be. \(\gamma \) represents the regulating factor to reduce the weight of the background, making the model focus on segmenting the objects in medical images. In our ...
Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion...
The loss function is the unweighted sum between dice loss and cross-entropy loss, which has been proven to be robust in various segmentation tasks1. The network was optimized by AdamW37 optimizer (β1 = 0.9, β2 = 0.999) with an initial learning rate of 1e-4 and a weight decay of ...
2.3.2 Weighted cross entropy loss The cross-entropy loss deals with each pixel of images equally, and thus outputs an average value, which ignores the class imbalance and leads to a problem that the loss function depends on the class including the maximal number of pixels. Therefore, the cros...
Patient had difficulty in eating and there was documented weight loss of 1.5 kg over three months period. Local examination demonstrated a single midline ulcer over the anterior two-third of the dorsal surface of the tongue (Figure 1). The ulcer was irregular in shape with everted edges, ...
The loss function is the unweighted sum between dice loss and cross-entropy loss, which has been proven to be robust in various segmentation tasks1. The network was optimized by AdamW37 optimizer (β1 = 0.9, β2 = 0.999) with an initial learning rate of 1e-4 and a weight decay of ...
the image decoded from compressed data has no information loss, which ensures the authenticity of medical images. This corresponds to the first property of medical images. In addition, soft compression algorithm uses the shape as the basic unit, reflecting the essential composition of an image. Thi...