Since the images and masks of the original Drone Images are very large (3.8K to 4.4K pixels width), we adopted the following Divide-and-Conquer Strategy for building our segmentation model. 1. Tiled ...
Accurate medical image segmentation is crucial in clinical applications. The existing Swin-UNet model overcomes the limitations of traditional Transformers in handling local details and high-frequency ...
Abstract: In the clinical diagnosis of prostate cancer, transrectal ultrasound (TRUS) is a commonly used examination method. Accurate segmentation of the prostate from TRUS images is crucial for ...
Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
1 School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, China 2 School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China ...
As shown above, the number of images of train and valid datasets is not so large to use for the training set of our segmentation model. We trained Ovarian-Tumor-3D TensorFlowFlexUNet Model by using ...
Background and objectives: This paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from ...
Google updated its Google image SEO best practices help document to recommend that you use the same image file name URL for the same image, even if you place that same image on different pages on your ...
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