Search Results for author: Krzysztof J. Geras

Found 25 papers, 16 papers with code

Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks

1 code implementation10 Feb 2022 Nan Wu, Stanisław Jastrzębski, Kyunghyun Cho, Krzysztof J. Geras

We propose an algorithm to balance the conditional learning speeds between modalities during training and demonstrate that it indeed addresses the issue of greedy learning.

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

1 code implementation13 Jun 2021 Kangning Liu, Yiqiu Shen, Nan Wu, Jakub Chłędowski, Carlos Fernandez-Granda, Krzysztof J. Geras

In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i. e. the location of a lesion.

Medical Diagnosis Weakly supervised segmentation

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction

1 code implementation13 Jan 2021 Anuroop Sriram, Matthew Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, William Moore

The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0. 742 for predicting an adverse event within 96 hours (compared to 0. 703 with supervised pretraining) and an AUC of 0. 765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0. 749 with supervised pretraining).

Representation Learning Self-Supervised Learning

Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability

1 code implementation19 Oct 2020 Jason Phang, Jungkyu Park, Krzysztof J. Geras

We find that the most important ingredients for high quality saliency map generation are (1) using both masked-in and masked-out objectives and (2) training the classifier alongside the masking model.

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

no code implementations19 Sep 2020 Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, S. Gene Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, Krzysztof J. Geras

Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost.

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

1 code implementation4 Aug 2020 Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

COVID-19 Diagnosis Decision Making +1

Understanding the robustness of deep neural network classifiers for breast cancer screening

no code implementations23 Mar 2020 Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski, Tomasz Trzciński, Linda Moy, Kyunghyun Cho, Laura Heacock, Krzysztof J. Geras

Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented.

Improving localization-based approaches for breast cancer screening exam classification

no code implementations1 Aug 2019 Thibault Févry, Jason Phang, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200, 000 exams (over 1, 000, 000 images).

Classification General Classification

Screening Mammogram Classification with Prior Exams

no code implementations30 Jul 2019 Jungkyu Park, Jason Phang, Yiqiu Shen, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.

Classification General Classification

Classifier-agnostic saliency map extraction

1 code implementation ICLR 2019 Konrad Zolna, Krzysztof J. Geras, Kyunghyun Cho

To address this problem, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance.

Computer Vision General Classification

Blending LSTMs into CNNs

no code implementations19 Nov 2015 Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Ozlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton

We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.

Automatic Speech Recognition Inductive Bias +2

Scheduled denoising autoencoders

1 code implementation12 Jun 2014 Krzysztof J. Geras, Charles Sutton

We present a representation learning method that learns features at multiple different levels of scale.

Denoising Representation Learning

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