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Found 29 papers, 19 papers with code
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue.
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels.
An efficient deep neural network to find small objects in large 3D images
On a dataset collected at NYU Langone Health, including 85, 526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0. 831 (95% CI: 0. 769-0. 887) in classifying breasts with malignant findings using 3D mammography.
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Multi-modal fusion approaches aim to integrate information from different data sources.
Ranked #1 on
Phenotype classification
on MIMIC-CXR, MIMIC-IV
Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks
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.
Generative multitask learning mitigates target-causing confounding
We propose generative multitask learning (GMTL), a simple and scalable approach to causal representation learning for multitask learning.
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data.
Recognizing and overcoming the greedy nature of learning in multi-modal deep neural networks
We refer to this gain as the conditional utilization rate of the modality.
Meta-repository of screening mammography classifiers
Artificial intelligence (AI) is showing promise in improving clinical diagnosis.
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
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.
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
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).
Differences between human and machine perception in medical diagnosis
We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions.
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
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
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
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.
Understanding the robustness of deep neural network classifiers for breast cancer screening
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.
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Improving the Ability of Deep Neural Networks to Use Information From Multiple Views in Breast Cancer Screening
In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles.
Improving localization-based approaches for breast cancer screening exam classification
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).
Screening Mammogram Classification with Prior Exams
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Moreover, both the global structure and local details play important roles in medical image analysis tasks.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
Classifier-agnostic saliency map extraction
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.
Breast density classification with deep convolutional neural networks
Breast density classification is an essential part of breast cancer screening.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
Blending LSTMs into CNNs
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Scheduled denoising autoencoders
We present a representation learning method that learns features at multiple different levels of scale.
