Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression

Although melanoma occurs more rarely than several other skin cancers, patients' long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that provides potential concordance information to healthcare providers could help inform diagnostic, prognostic, and therapeutic decision-making for challenging melanoma cases. We present a melanoma concordance regression deep learning model capable of predicting the concordance rate of invasive melanoma or melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features corresponding to melanoma concordance were learned in a self-supervised manner with the contrastive learning method, SimCLR. We trained a SimCLR feature extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens originating from four distinct pathology labs. We trained a separate melanoma concordance regression model on 990 specimens with available concordance ground truth annotations from three pathology labs and tested the model on 211 specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on the test set. We also investigated the performance of using the predicted concordance rate as a malignancy classifier, and achieved a precision and recall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. These results are an important first step for building an artificial intelligence (AI) system capable of predicting the results of consulting a panel of experts and delivering a score based on the degree to which the experts would agree on a particular diagnosis. Such a system could be used to suggest additional testing or other action such as ordering additional stains or genetic tests.Comment: Accepted at ECCV 2022 AIMIA Workshop. arXiv admin note: text overlap with arXiv:2109.0755

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

Electrophysiological measurement of information flow during visual search

Abstract The temporal relationship between different stages of cognitive processing is long debated. This debate is ongoing, primarily because it is often difficult to measure the time course of multiple cognitive processes simultaneously. We employed a manipulation that allowed us to isolate ERP components related to perceptual processing, working memory, and response preparation, and then examined the temporal relationship between these components while observers performed a visual search task. We found that, when response speed and accuracy were equally stressed, our index of perceptual processing ended before both the transfer of information into working memory and response preparation began. However, when we stressed speed over accuracy, response preparation began before the completion of perceptual processing or transfer of information into working memory on trials with the fastest reaction times. These findings show that individuals can control the flow of information transmission between stages, either waiting for perceptual processing to be completed before preparing a response or configuring these stages to overlap in time. Descriptors: Attention, ERPs, Visual processes, N2pc, LRP, Speed accuracy trade-off One of the oldest debates in psychology centers on the temporal relationship between cognitive operations. For example, it has been hypothesized that responding appropriately to an object that we encounter requires information to be processed in a sequence of discrete stages in which one stage must finish before the next can begin (Donders, 1868 The goal of the current study was to determine whether evidence for continuous information flow can be observed during visual search and, if so, to specify precisely which cognitive operations can be configured to overlap with one another in time using ERPs. The ERP technique is uniquely suited to address these questions because discrete ERP components have been shown to measure discrete aspects of cognition and are temporally precise, indexing the earliest and latest time points at which the underlying cognitive processes are operative To overcome this problem and enable the ability to directly compare components related to discrete processing stages, we examined the time course of two components that can be distinguished by their lateralized distributions, the perceptual attentionrelated N2pc and the response-related lateralized readiness potential (LRP), alongside a nonlateralized measure of the transfer of information into working memory, the P3b. The goal was to directly examine the temporal relationship between these components in order to provide a window into the temporal unfolding of cognitive processes from perceptual processing through response preparation in a typical visual search task The N2pc is lateralized on the scalp relative to the locus of spatial attention, and previous research in visual search tasks has demonstrated that the onset of the N2pc can be used to track when perceptual-level attention is deployed to an object. Critically, during search the N2pc is directly followed by the onset of a lateralized positivity (the Pd), which signals the termination of perceptua