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Aditya Khosla
Researcher at Massachusetts Institute of Technology
Publications - 62
Citations - 71575
Aditya Khosla is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Object detection & Artificial neural network. The author has an hindex of 39, co-authored 61 publications receiving 50417 citations. Previous affiliations of Aditya Khosla include Stanford University & Open University of Catalonia.
Papers
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Book ChapterDOI
Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities
TL;DR: This chapter proposes a random forest with discriminative decision trees algorithm where every tree node is a discrim inative classifier that is trained by combining the information in this node as well as all upstream nodes.
Posted ContentDOI
Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer
James A. Diao,Wan Fung Chui,Jason K Wang,Richard N. Mitchell,Richard N. Mitchell,Sudha K. Rao,Murray B. Resnick,Abhik Lahiri,Chirag Maheshwari,Benjamin Glass,Victoria Mountain,Jennifer K. Kerner,Michael Christopher Montalto,Aditya Khosla,Ilan Wapinski,Andrew H. Beck,Amaro Taylor-Weiner,Hunter L. Elliott +17 more
TL;DR: This study presents a novel approach for predicting clinically-relevant molecular phenotypes from histopathology whole-slide images (WSIs) using human-interpretable image features (HIFs) and demonstrates that these HIFs correlate with well-known markers of the tumor microenvironment (TME) and can predict diverse molecular signatures.
Posted Content
Following Gaze Across Views.
TL;DR: An approach for following gaze across views by predicting where a particular person is looking throughout a scene by building an end-to-end model that solves the following sub-problems: saliency, gaze pose, and geometric relationships between views.
Journal ArticleDOI
What makes a picture memorable
TL;DR: In this article, the authors developed an algorithm that automatically predicts whether an image will be memorable and found that visual memorability is largely intrinsic to the image and reproducible across a diverse population.
Patent
Systems and methods for training a model to predict survival time for a patient
Andrew H. Beck,Aditya Khosla +1 more
TL;DR: In this paper, the authors proposed a method for training a model to predict survival time for a patient using annotated pathology images associated with a first group of patients in a clinical trial.