Krzysztof J. Geras

Affiliations:
  • New York University, Center for Data Science, NY, USA


According to our database1, Krzysztof J. Geras authored at least 34 papers between 2012 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Online presence:

On csauthors.net:

Bibliography

2022
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images.
CoRR, 2022

Generative multitask learning mitigates target-causing confounding.
CoRR, 2022

Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks.
Proceedings of the International Conference on Machine Learning, 2022

2021
Lessons from the first DBTex Challenge.
Nat. Mach. Intell., 2021

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.
Medical Image Anal., 2021

Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms.
J. Digit. Imaging, 2021

Towards dynamic multi-modal phenotyping using chest radiographs and physiological data.
CoRR, 2021

Meta-repository of screening mammography classifiers.
CoRR, 2021

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction.
CoRR, 2021

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis.
Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.
IEEE Trans. Medical Imaging, 2020

Classifier-agnostic saliency map extraction.
Comput. Vis. Image Underst., 2020

Differences between human and machine perception in medical diagnosis.
CoRR, 2020

Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability.
CoRR, 2020

Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms.
CoRR, 2020

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.
CoRR, 2020

Understanding the robustness of deep neural network classifiers for breast cancer screening.
CoRR, 2020

Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

The Break-Even Point on Optimization Trajectories of Deep Neural Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Improving localization-based approaches for breast cancer screening exam classification.
CoRR, 2019

Screening Mammogram Classification with Prior Exams.
CoRR, 2019

Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.
Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

2018
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.
CoRR, 2018

Breast Density Classification with Deep Convolutional Neural Networks.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks.
CoRR, 2017

Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?
CoRR, 2016

Composite Denoising Autoencoders.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Scheduled denoising autoencoders.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Compressing LSTMs into CNNs.
CoRR, 2015

2013
Multiple-source cross-validation.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Isoelastic Agents and Wealth Updates in Machine Learning Markets.
Proceedings of the 29th International Conference on Machine Learning, 2012


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