A method for processing an image includes computing a thresholding score for an image using a plurality of threshold values to provide a plurality of thresholding scores for the image, determining a selected threshold value based on the plurality of thresholding scores, processing the image or ...
Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical data remains unclear. In this work, we first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2)...
Most plain CT is acquired as a thin section (1–2 mm) contiguous volume image, however the signal to noise ratio in such images is too low for diagnostic use in stroke with 3–6 mm reconstructions being more commonly used in stroke diagnosis. Many of the more sophisticated image processing...
To first understand why long-range dependencies matter for medical images, we visualize an example ultrasound scan of a preterm neonate and segmentation predictions of brain ventricles from the scan in Fig 1. For a network to provide an efficient segmentation, it should be able to understand ...
Advanced Generative Deep Learning Techniques for Accurate Captioning of Images J. Navin Chandar G. Kavitha Wireless Personal Communications(2024) Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis Huizi Yu ...
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Therefore, in the patch-wise model training, all patches, which are completely labeled as background, can be excluded in order to avoid wasting time on unnecessary fitting. Batch management After the data preprocessing and the optional data augmentation for training, sets of full images or ...
The FCN which has been trained on the whole 3D images has high class imbalance between the foreground and background, which resulted into inaccurate segmentation of small organs [64,94]. One possible solution to alleviate this issue is applying two-step segmentation in a hierarchical manner, wher...
In this figure, the distributions of the grayscale values in the background of image (a) and the object of image (b) are inhomogeneous. For these kinds of images, it is easy to incorrectly segment the boundary at the position where the grayscale value of the object is similar to that ...
While training the CNN architecture, the model predicts the class scores for training images, computes the loss using the selected loss function and finally updates the weights using the gradient descent method by back-propagation. The cross-entropy loss is one of the most widely used loss ...