Slice Detection

Slice detection focuses on identifying individual slices within a 3D image dataset that contain relevant information, such as the presence of a tumor or a specific anatomical feature. Current research employs deep learning models, particularly convolutional neural networks (CNNs), often leveraging techniques like contrastive metric learning and ResNet architectures, to classify slices based on features extracted from the image data itself or from neighboring slices. This automated approach has significant implications for improving efficiency in medical image analysis, accelerating diagnosis, and reducing the cognitive load on clinicians by prioritizing the most informative image sections for review in fields like radiology and pathology, and also for improving the understanding and performance of NLP models.