There is a large amount of out-of-distribution (OOD) data in remote sensing, which hinders high-accuracy segmentation models under the assumption of independent identical distribution (i.i.d.) from ...

Abstract: The data-intensive nature of supervised classification drives the interest of the researchers towards unsupervised approaches, especially for problems such as medical image segmentation, ...

Introduction: Accurate automated segmentation of epistaxis (nosebleeds) from endoscopic images is critical for clinical diagnosis but is significantly hampered by the scarcity of annotated data and ...

1 School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China 2 School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, ...

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 ...

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 ...

Brain tumor detection and segmentation are critical tasks in medical imaging analysis for diagnosis and treatment planning. In recent years, computer vision techniques, particularly those implemented ...

This project implements semantic image segmentation using two popular convolutional neural network architectures: U-Net and SegNet. Semantic image segmentation involves partitioning an image into ...