Search Results for author:
Found 6 papers, 2 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.
Self-supervised co-salient object detection via feature correspondence at multiple scales
Extensive experiments on three CoSOD benchmark datasets show that our self-supervised model outperforms the corresponding state-of-the-art models by a huge margin (e. g. on the CoCA dataset, our model has a 13. 7% F-measure gain over the SOTA unsupervised CoSOD model).
Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics
Our unsupervised model is a great pre-training initialization for our semi-supervised model SS-CoSOD, especially when very limited labeled data is available for training.
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.
Capturing Financial markets to apply Deep Reinforcement Learning
In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market.
A dense subgraph based algorithm for compact salient image region detection
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image.
