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Found 5 papers, 1 papers with code
Decoding the visual attention of pathologists to reveal their level of expertise
We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading.
Reconstruction-guided attention improves the robustness and shape processing of neural networks
Ablation studies further reveal two complementary roles of spatial and feature-based attention in robust object recognition, with the former largely consistent with spatial masking benefits in the attention literature (the reconstruction serves as a mask) and the latter mainly contributing to the model's inference speed (i. e., number of time steps to reach a certain confidence threshold) by reducing the space of possible object hypotheses.
Visual attention analysis of pathologists examining whole slide images of Prostate cancer
To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Using machine learning and the psychologically-meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
Studying Relationships between Human Gaze, Description, and Computer Vision
We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance.
