AlphaGenome is a leap forward in the ability to study the human blueprint. But the fine workings of our DNA are still largely ...
The line between human and artificial intelligence is growing ever more blurry. Since 2021, AI has deciphered ancient texts ...
Semantic segmentation is critical in medical image processing, with traditional specialist models facing adaptation challenges to new tasks or distribution shifts. While both generalist pre-trained ...
Abstract: Unsupervised brain lesion segmentation, focusing on learning normative distributions from images of healthy subjects, are less dependent on lesion-labeled data, thus exhibiting better ...
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 ...
Purpose: Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive ...
Recent research has focused on multimodal medical image segmentation. A cascaded V-net and H-DenseUNet approach have improved Dice scores, but at the expense of high computational complexity.
ABSTRACT: In this paper, a novel multilingual OCR (Optical Character Recognition) method for scanned papers is provided. Current open-source solutions, like Tesseract, offer extremely high accuracy ...
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