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This fully updated medical reference book contains chapters covering general pathology, the major organ systems, and ancillary diagnostic techniques, as well as important topics including immunohistochemistry, cytopathology, and molecular diagnostics.
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Published in: Full Name Comment goes here. Are you sure you want to Yes No. Be the first to like this. No Downloads. Views Total views. By contrast, a naive implementation on a single machine with image patches times fewer than above required a significantly slower 25 s per query. To our knowledge, we have performed the most comprehensive evaluation of a reverse image search tool for histopathology. SMILY retrieves image search results with similar histologic features, organ site, and cancer grades, based on both large-scale quantitative analysis using annotated tissue regions and prospective studies with pathologists blinded to the source of the search results.
From first principles, the ideal search tool displays what you are searching for. However, this goal is ambiguous because the intent of the search depends on the use case: searching for other images with the same stain, similar stain intensity, same histologic feature, or similar lesion in the most general sense. As such, in the absence of information about search intent, the ideal tool should surface a breadth of search results instead of focusing on any single axis of similarity.
To address the lack of algorithmic awareness for the search intent, advances in human-computer interaction may enable interactive refinement of search results based on certain desired axes of similarity. In diagnosis, SMILY could be a helpful tool to search for similar lesions within the same slide or in other patients. For research, a clinician might have a hypothesis: occurrence of a certain histopathological feature in the slide is correlated with clinical endpoints.
However, an adequately powered study may require a large number of patients, rendering the manual search for these features highly labor intensive. SMILY could enable significant speedups in this search via computer-assisted search. Finally, trainees are frequently confronted with unknown lesions.
Manual searching of pathology textbooks, atlases, and other resources for similar lesions can be time-consuming; SMILY could reduce this process to an image-based query and manual assessment for the most relevant result.
Importantly, these searches could also leverage large publicly available databases such as the TCGA, as we have done here. Indeed, with respect to specific applications such as mitotic counting, 26 approaches that have been developed specifically for that application may result in higher accuracy for that purpose.
However, developing and implementing specific but separate approaches for every possible task of interest is impractical. Some challenges are: expensive data collection and labeling, difficulty of workflow integration and potential legal or commercial issues, and lack of machine learning, software or hardware expertize for development or implementation.
As such, the availability of a general-purpose tool like SMILY that can be used in multiple applications, can be helpful despite having lower accuracy than an application-specific tool.
An interesting aspect of SMILY is that the core neural network algorithm was not trained using histopathology images. Instead, the network was trained using a dataset of images, including people, animals, and man-made and natural objects see Methods. CBIR has been studied extensively in medical imaging, 9 , 10 , 30 and in histopathology for both slides 14 , 18 and image patches. These annotations also in turn restrict the concept of similarity to be along a few predetermined axes, such as cancer grade and histologic features.
Specifically, although the neural network underlying SMILY was trained using supervised learning based on non-histopathological images Methods , the histopathology annotations we collected were used exclusively for evaluating SMILY and not training.
The large size of each histopathology image and the scale of typical histopathology databases — images raise important technical considerations for real-world use. First, the embedding of each patch needs to be calculated as a one-time computation cost.
This incurs a delay to compute the embeddings for the — patches in each newly digitized slide before the slide can be searched across.
Second, these embeddings need to be stored to avoid repeated embedding computation. Although this overhead was only 0.
Finally, the search phase requires comparing the query image embedding with millions or billions of other embeddings.
For example, a naive implementation of this process on the entirety of the publicly available The Cancer Genome Atlas TCGA, contains over 33, slides dataset 33 will incur an impractical, half-minute latency on a modern desktop computer. To support real-world usage, we have optimized this process to require only seconds on a web interface Methods.
This study contains limitations, such as those discussed in-depth above regarding accuracy of a similar image search tool and limitation of a general-purpose search tool versus application-specific tools. In addition, the number of slides could be increased to better capture the breadth of tissue processing conditions and resulting images.
Methods Neural network architecture SMILY is based on a convolutional neural network architecture called a deep ranking network.
This module contains layers of convolutional, pooling, and concatenation operations. The network then uses the modules to compute the embeddings of each of the three images. In this way, the network learned to distinguish similar images from dissimilar ones by computing and comparing the embeddings of input images.