2 天之前 · Suk, Deep learning in medical image analysis, Annu. Rev. Biomed. Eng. 19 (1) (2017) 221–248. Google Scholar [19] A. Suganya, S. Aarthy, Deep learning in medical image classification, in Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics , CRC Press, 2021, pp. 139–169. Google Scholar

2025年1月2日 · Image semantic segmentation is a task that involves dividing an image into multiple regions or segments and assigning a label to each segment. The goal is to classify each...

2019年5月27日 · Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning...

1 天前 · Artificial intelligence (AI) has transformed digital pathology by enabling biomarker prediction from high-resolution whole slide images (WSIs). However, current methods are computationally inefficient, processing thousands of redundant tiles per WSI and requiring complex aggregator models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that ...

5 天之前 · Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image ...

The proposed model presents a three-phase approach including image analysis, synthesis, and indexing to improve image retrieval efficiency and accuracy by integrating deep features of CNN models.

2025年1月8日 · To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional...

This paper provides a thorough review of deep learning techniques in image and video analysis, focusing on multi-object tracking, convolution and recurrent neural networks, image matting, video recognition, and applications such as object detection.

2023年8月2日 · We introduce DLSIA (Deep Learning for Scientific Image Analysis), a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing ...

2024年11月27日 · Deep learning has gained significant attention in remote sensing, especially in pixel- or patch-level applications. Despite initial attempts to integrate deep learning into object-based image analysis (OBIA), its full potential remains largely unexplored.