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Aditya Khosla

Aditya Khosla

D-Index & Metrics

Computer Science

D-Index
35
Citations
80749
World Ranking
11411
National Ranking
4687

Overview

Aditya Khosla is affiliated with MIT in the United States and has focused their research primarily on intersections of medicine and computer science. Their scholarly output includes works that span fields such as radiology, nuclear medicine and imaging, artificial intelligence, oncology, genetics, and cancer research. The central themes of their work concentrate especially on radiomics and machine learning applied to medical imaging as well as the use of AI in cancer detection.

Their main topics of research cover:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Inflammatory Bowel Disease
  • Colorectal Cancer Screening and Detection
  • Cancer Genomics and Diagnostics
  • Genetic factors in colorectal cancer
  • Cancer Cells and Metastasis

Among their recent papers are:

  • "Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology" (2023) published in Modern Pathology
  • "Distinct hepatic immunological patterns are associated with the progression or inhibition of hepatocellular carcinoma" (2022) in Cell Reports
  • "AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis" (2024) featured in npj Precision Oncology
  • "Abstract 5099: Quantification of TGFβ protein levels and digital pathology-based immune phenotyping reveal biomarkers for TGF-β blockade therapy patient selection in NSCLC" (2022) found in Cancer Research
  • "Improved statistical benchmarking of digital pathology models using pairwise frames evaluation" (2023) in arXiv (Cornell University)

Khosla collaborates frequently with several researchers, including Ilan Wapinski, Michael G. Drage, Fedaa Najdawi, Amaro Taylor-Weiner, and Jacqueline A. Brosnan-Cashman. These collaborations have resulted in multiple co-authored publications.

Their work has appeared repeatedly in well-established venues such as Cancer Research, bioRxiv (Cold Spring Harbor Laboratory), Gastroenterology, Regular and Young Investigator Award Abstracts, and Modern Pathology, indicating a consistent presence in both clinical and preprint research platforms.

Best Publications

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

  • Learning Deep Features for Discriminative Localization

    Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva

  • 3D ShapeNets: A deep representation for volumetric shapes

    Zhirong Wu;Shuran Song;Aditya Khosla;Fisher Yu

  • Places: A 10 Million Image Database for Scene Recognition

    Bolei Zhou;Agata Lapedriza;Aditya Khosla;Aude Oliva

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • Multimodal Deep Learning

    Jiquan Ngiam;Aditya Khosla;Mingyu Kim;Juhan Nam

  • Network Dissection: Quantifying Interpretability of Deep Visual Representations

    David Bau;Bolei Zhou;Aditya Khosla;Aude Oliva

  • Object Detectors Emerge in Deep Scene CNNs

    Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva

  • Eye Tracking for Everyone

    Kyle Krafka;Aditya Khosla;Petr Kellnhofer;Harini Kannan

  • Deep Learning for Identifying Metastatic Breast Cancer

    Dayong Wang;Aditya Khosla;Rishab Gargeya;Humayun Irshad

  • Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    Radoslaw Martin Cichy;Aditya Khosla;Dimitrios Pantazis;Antonio Torralba

  • Human action recognition by learning bases of action attributes and parts

    Bangpeng Yao;Xiaoye Jiang;Aditya Khosla;Andy Lai Lin

  • Undoing the damage of dataset bias

    Aditya Khosla;Tinghui Zhou;Tomasz Malisiewicz;Alexei A. Efros

  • What makes an image popular

    Aditya Khosla;Atish Das Sarma;Raffay Hamid

  • Places: An Image Database for Deep Scene Understanding

    Bolei Zhou;Aditya Khosla;Àgata Lapedriza;Antonio Torralba

  • HOGgles: Visualizing Object Detection Features

    Carl Vondrick;Aditya Khosla;Tomasz Malisiewicz;Antonio Torralba

  • Combining randomization and discrimination for fine-grained image categorization

    Bangpeng Yao;Aditya Khosla;Li Fei-Fei

  • Understanding and Predicting Image Memorability at a Large Scale

    Aditya Khosla;Akhil S. Raju;Antonio Torralba;Aude Oliva

  • Large-Scale Video Summarization Using Web-Image Priors

    Aditya Khosla;Raffay Hamid;Chih-Jen Lin;Neel Sundaresan

  • Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

    James A. Diao;Jason K. Wang;Wan Fung Chui;Victoria Mountain

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

Frequent Co-Authors

Bolei Zhou
Bolei Zhou University of California, Los Angeles
Carl Vondrick
Carl Vondrick Columbia University
Jianxiong Xiao
Jianxiong Xiao AutoX, Inc.
Li Fei-Fei
Li Fei-Fei Stanford University
Hao Su
Hao Su University of California, San Diego
Michael S. Bernstein
Michael S. Bernstein Stanford University
Jia Deng
Jia Deng Princeton University
Alexander C. Berg
Alexander C. Berg University of North Carolina at Chapel Hill

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