no code implementations • 25 Mar 2024 • Souradeep Chakraborty, Dana Perez, Paul Friedman, Natallia Sheuka, Constantin Friedman, Oksana Yaskiv, Rajarsi Gupta, Gregory J. Zelinsky, Joel H. Saltz, Dimitris Samaras
We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading.
1 code implementation • 17 Mar 2024 • Souradeep Chakraborty, Dimitris Samaras
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).
no code implementations • 11 Nov 2023 • Souradeep Chakraborty, Shujon Naha, Muhammet Bastan, Amit Kumar K C, Dimitris Samaras
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.
no code implementations • 17 Feb 2022 • Souradeep Chakraborty, Ke Ma, Rajarsi Gupta, Beatrice Knudsen, Gregory J. Zelinsky, Joel H. Saltz, Dimitris Samaras
To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist.
no code implementations • 9 Jul 2019 • Souradeep Chakraborty
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.
1 code implementation • 20 Nov 2015 • Souradeep Chakraborty, Pabitra Mitra
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image.