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

Automated Protein Localization of Blood Brain Barrier Vasculature in Brightfield IHC Images.

In this paper, we present an objective method for localization of proteins in blood brain barrier (BBB) vasculature using standard immunohistochemistry (IHC) techniques and bright-field microscopy. Images from the hippocampal region at the BBB are acquired using bright-field microscopy and subjected to our segmentation pipeline which is designed to automatically identify and segment microvessels containing the protein glucose transporter 1 (GLUT1). Gabor filtering and k-means clustering are employed to isolate potential vascular structures within cryosectioned slabs of the hippocampus, which are subsequently subjected to feature extraction followed by classification via decision forest. The false positive rate (FPR) of microvessel classification is characterized using synthetic and non-synthetic IHC image data for image entropies ranging between 3 and 8 bits. The average FPR for synthetic and non-synthetic IHC image data was found to be 5.48% and 5.04%, respectively

False negative classification rate.

<p>FNR of microvessel classification for synthetic data as a function of image entropy. Average FNR = 7.49.</p

Execution time profile for the automated protein localization algorithm.

<p>Execution time profile for the automated protein localization algorithm.</p

Example result given by the segmentation algorithm.

<p>Shown is an IHC image plane within the bregma stained for GLUT1 expression in vascular structures. Green contours identify microvessels exhibiting significant GLUT1 concentration.</p

Real parts of the Gabor filter bank.

<p>Generated for different combinations of <i>θ</i> (in radians) and <i>f</i> (in Hz) with , and <i>ϕ</i> = 0.</p

Overview of the workflow.

<p>Input immunohistochemistry (IHC) images are pre-segmented to identify candidate structures of interest, which are represented within a generated mask image. Candidate structures within the mask image are filtered using a decision tree derived from training sessions to produce a fully segmented IHC image. For further details, see text.</p

GLUT1 stained image examples.

<p>(top) Synthetic and (bottom) non-synthetic images with varying global pixel entropy (<i>H</i>) Local spatial frequency tends to increase with local entropy. IHC images with higher <i>H</i> usually exhibit more spatially complex surface geometries and/or possess increased surface noise due to the staining protocol.</p

Pixel-wise classification of the hippocampal region using k-means clustering.

<p>White pixels mark regions potentially containing vascular structures of interest. Black pixels mark non-vascular structures.</p