30,027 research outputs found
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets of
dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human
Against Machine with 10000 training images") dataset. We collected
dermatoscopic images from different populations acquired and stored by
different modalities. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy
Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning
Postprint (published version
Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.
Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system\u27s use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications
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