bibtext: @InProceedings{ Cha_Decoding_MICCAI2024,
                   author = { Chakraborty, Souradeep and Gupta, Rajarsi and Yaskiv, Oksana and Friedman, Constantin and Sheuka, Natallia and Perez, Dana and Friedman, Paul and Zelinsky, Gregory and Saltz, Joel and Samaras, Dimitris },
                   title = { { Decoding the visual attention of pathologists to reveal their level of expertise } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading. We engage this decoding task by developing a novel method for predicting the attention of pathologists as they read Whole-Slide Images (WSIs) of prostate tissue and make cancer grade classifications. Our ground truth measure of a pathologists’ attention is the x, y and z (magnification) movement of their viewport as they navigated through WSIs during readings, and to date we have the attention behavior of 43 pathologists reading 123 WSIs. These data revealed that specialists have higher agreement in both their attention and cancer grades compared to general pathologists and residents, suggesting that sufficient information may exist in their attention behavior to classify their expertise level. To attempt this, we trained a transformer-based model to predict the visual attention heatmaps of resident, general, and specialist (Genitourinary) pathologists during Gleason grading. Based solely on a pathologist’s attention during a reading, our model was able to predict their level of expertise with 75.3%, 56.1%, and 77.2% accuracy, respectively, better than chance and baseline models. Our model therefore enables a pathologist’s expertise level to be easily and objectively evaluated, important for pathology training and competency assessment. Tools developed from our model could be used to help pathology trainees learn how to read WSIs like an expert.
title: Decoding the visual attention of pathologists to reveal their level of expertise
authors: Chakraborty, Souradeep Gupta, Rajarsi Yaskiv, Oksana Friedman, Constantin Sheuka, Natallia Perez, Dana Friedman, Paul Zelinsky, Gregory Saltz, Joel Samaras, Dimitris