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D-Index & Metrics

Computer Science

D-Index
37
Citations
10152
World Ranking
10490
National Ranking
4390

Overview

Christopher Kanan is affiliated with the University of Rochester in the United States. Their research primarily spans the field of computer science, with a significant focus on artificial intelligence. This includes specialized work in computer vision and pattern recognition, radiology, nuclear medicine and imaging, pulmonary and respiratory medicine, and cognitive neuroscience.

The scientist's work addresses several interconnected topics, including domain adaptation and few-shot learning, AI applications in cancer detection, radiomics and machine learning in medical imaging, multimodal machine learning applications, advanced neural network applications, adversarial robustness in machine learning, and machine learning and data classification.

Christopher Kanan has contributed to numerous publications, with a recorded output of over a hundred papers, many appearing in venues such as arXiv (Cornell University), where they have published 37 papers. Other notable publication venues include Cancer Research, Journal of Clinical Oncology, Nature Medicine, and Modern Pathology.

Recent papers authored or coauthored by Kanan include the following:

  • "A foundation model for clinical-grade computational pathology and rare cancers detection" (2024, Nature Medicine)
  • "Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies" (2020, Modern Pathology)
  • "Independent real-world application of a clinical-grade automated prostate cancer detection system" (2021, The Journal of Pathology)
  • "Avalanche: An end-to-end library for continual learning" (2021, CINECA IRIS Institutional research information system, University of Pisa)
  • "Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities" (2020, Scientific Reports)

Frequent coauthors collaborating with Christopher Kanan include:

  • Tyler L. Hayes
  • Ran Godrich
  • Robik Shrestha
  • Adam Casson
  • Thomas J. Fuchs

Best Publications

  • Continual lifelong learning with neural networks: A review.

    German Ignacio Parisi;Ronald Kemker;Jose L. Part;Christopher Kanan

  • Measuring Catastrophic Forgetting in Neural Networks

    Unknown

  • Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

    Unknown

  • Robotic grasp detection using deep convolutional neural networks

    Unknown

  • Color-to-Grayscale: Does the Method Matter in Image Recognition?

    Christopher Kanan;Garrison W. Cottrell

  • Visual question answering: Datasets, algorithms, and future challenges

    Unknown

  • An Analysis of Visual Question Answering Algorithms

    Unknown

  • REMIND Your Neural Network to Prevent Catastrophic Forgetting

    Tyler L. Hayes;Kushal Kafle;Robik Shrestha;Manoj Acharya

  • Robust classification of objects, faces, and flowers using natural image statistics

    Christopher Kanan;Garrison Cottrell

  • DVQA: Understanding Data Visualizations via Question Answering

    Kushal Kafle;Brian Price;Scott Cohen;Christopher Kanan

  • Memory Efficient Experience Replay for Streaming Learning

    Tyler L. Hayes;Nathan D. Cahill;Christopher Kanan

  • VAIS: A dataset for recognizing maritime imagery in the visible and infrared spectrums

    Mabel M. Zhang;Jean Choi;Kostas Daniilidis;Michael T. Wolf

  • Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

    Patricia Raciti;Jillian Sue;Rodrigo Ceballos;Ran Godrich

  • Independent real-world application of a clinical-grade automated prostate cancer detection system.

    Leonard M da Silva;Emilio M Pereira;Paulo Go Salles;Ran Godrich

  • Data Augmentation for Visual Question Answering

    Unknown

  • Self-Taught Feature Learning for Hyperspectral Image Classification

    Unknown

  • Answer-Type Prediction for Visual Question Answering

    Unknown

  • Lifelong Machine Learning With Deep Streaming Linear Discriminant Analysis

    Tyler L. Hayes;Christopher Kanan

  • Avalanche: an End-to-End Library for Continual Learning

    Vincenzo Lomonaco;Lorenzo Pellegrini;Andrea Cossu;Antonio Carta

  • Replay in Deep Learning: Current Approaches and Missing Biological Elements

    Tyler L. Hayes;Giri P. Krishnan;Maxim Bazhenov;Hava T. Siegelmann

  • Humans have idiosyncratic and task-specific scanpaths for judging faces.

    Christopher Kanan;Dina N.F. Bseiso;Nicholas A. Ray;Janet H. Hsiao

  • On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

    Damien Teney;Kushal Kafle;Robik Shrestha;Ehsan Abbasnejad

  • Answer Them All! Toward Universal Visual Question Answering Models

    Robik Shrestha;Kushal Kafle;Christopher Kanan

  • Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

    Ronald Kemker;Ryan Luu;Christopher Kanan

  • A negative case analysis of visual grounding methods for VQA

    Robik Shrestha;Kushal Kafle;Christopher Kanan

  • Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities.

    Rakshit Sunil Kothari;Zhizhuo Yang;Christopher Kanan;Reynold Bailey

  • RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking

    Aayush K. Chaudhary;Rakshit Kothari;Manoj Acharya;Shusil Dangi

  • On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

    Damien Teney;Ehsan Abbasnejad;Kushal Kafle;Robik Shrestha

Frequent Co-Authors

Andreas S. Tolias
Andreas S. Tolias Baylor College of Medicine
Brian Price
Brian Price Adobe Systems (United States)
Davide Maltoni
Davide Maltoni University of Bologna
Hava T. Siegelmann
Hava T. Siegelmann University of Massachusetts Amherst
Sarat Chandarlapaty
Sarat Chandarlapaty Memorial Sloan Kettering Cancer Center
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Maxim Bazhenov
Maxim Bazhenov University of California, San Diego
Scott Cohen
Scott Cohen Adobe Systems (United States)

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