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Wednesday, October 19, 2022

Fast and Scalable Search of Images

Image Search has many useful applications, notably health care

Harvard Researchers Propose a Self-Supervised Deep Learning Algorithm for Fast and Scalable Search of Whole-Slide Images

By Mahmoud Ghorbel- October 14, 2022  in MarktechPost

The necessity for accurate and economical gigapixel image analysis has risen as whole-slide imaging has become more widely used. Deep learning is at the forefront of computer vision, showing considerable advancements in visual comprehension over earlier approaches. However, whole-slide images (WSI) include billions of pixels and are plagued by many sorts of artifacts as well as significant morphological variation. All of these work against the traditional usage of deep learning. These challenges must be overcome for the clinical translation of deep learning solutions to become a reality.

Most computational pathology approaches use supervised deep learning with slide- or case-level labels to address classification or ranking issues. For many applications, an image search engine that uses the detailed, spatially resolved information in pathology images is far more potent. However, scalability poses a significant obstacle to the widespread and effective use of histology whole-slide image search and retrieval systems. Compared to other image databases, this presents a challenging issue for the WSI retrieval system since it must effectively search an increasing number of slides, each of which may contain billions of pixels and be several gigabytes. Since WSIs are too large to process computationally, most methods either divide them into smaller image patches or concentrate on patch or region of interest (ROI) retrieval that is specialized for specific purposes. Recently, a new article published in the journal nature biomedical engineering suggests a search pipeline called self-supervised image search for histology (SISH) to overcome the problems listed above.

Regardless of the repository size, SISH searches for and retrieves WSIs quickly. It uses a tree data structure for quick searching and only needs slide-level annotations for training. It transforms WSIs into useful discrete latent representations and then ranks them according to an algorithm based on uncertainty. To decrease storage and labeling costs, SISH specifically draws on a collection of preprocessed mosaics from WSIs without pixel-wise or ROI-level labels by relying on indices discovered by self-supervised learning and pretrained embeddings. The proposed approach leverages discrete latent codes from a Vector Quantized-Variational AutoEncoder (VQ-VAE) in addition to the guided search  ... ' 

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