Recent advances in Natural Language Processing (NLP), and specifically automated Question Answering (QA) systems, have demonstrated both impressive linguistic fluency and a pernicious tendency to reflect social biases.
no code implementations • 28 Jun 2021 • Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P. Lungren, Andrew Y. Ng, Curtis P. Langlotz, Pranav Rajpurkar
We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.
Recent urbanization has coincided with the enrichment of geotagged data, such as street view and point-of-interest (POI).
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram.
Next, after training image classification models using labels generated from the different radiology report labelers on one of the largest datasets of chest X-rays, we show that an image classification model trained on labels from the VisualCheXbert labeler outperforms image classification models trained on labels from the CheXpert and CheXbert labelers.
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources.
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes.
Second, we evaluate whether models trained on seen diseases can detect seen diseases when co-occurring with diseases outside the subset (unseen diseases).
We also find that VisualCheXbert better agrees with radiologists labeling chest X-ray images than do radiologists labeling the corresponding radiology reports by an average F1 score across several medical conditions of between 0. 12 (95% CI 0. 09, 0. 15) and 0. 21 (95% CI 0. 18, 0. 24).
Medical image segmentation models are typically supervised by expert annotations at the pixel-level, which can be expensive to acquire.
Our controlled experiments show that the keys to improving downstream performance on disease classification are (1) using patient metadata to appropriately create positive pairs from different images with the same underlying pathologies, and (2) maximizing the number of different images used in query pairing.
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise.
First, we find no relationship between ImageNet performance and CheXpert performance for both models without pretraining and models with pretraining.
Using 0. 1% of labeled training data, we find that a linear model trained on MoCo-pretrained representations outperforms one trained on representations without MoCo-pretraining by an AUC of 0. 096 (95% CI 0. 061, 0. 130), indicating that MoCo-pretrained representations are of higher quality.
no code implementations • 14 Nov 2020 • Hao Sheng, Jeremy Irvin, Sasankh Munukutla, Shawn Zhang, Christopher Cross, Kyle Story, Rose Rustowicz, Cooper Elsworth, Zutao Yang, Mark Omara, Ritesh Gautam, Robert B. Jackson, Andrew Y. Ng
In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions.
In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays.
Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies.
The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides.
In this work, we propose MoCo-CXR, which is an adaptation of the contrastive learning method Momentum Contrast (MoCo), to produce models with better representations and initializations for the detection of pathologies in chest X-rays.
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.
We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus.
1 code implementation • 13 Jul 2020 • Nick A. Phillips, Pranav Rajpurkar, Mark Sabini, Rayan Krishnan, Sharon Zhou, Anuj Pareek, Nguyet Minh Phu, Chris Wang, Mudit Jain, Nguyen Duong Du, Steven QH Truong, Andrew Y. Ng, Matthew P. Lungren
We introduce CheXphoto, a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheXpert dataset.
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.
The extraction of labels from radiology text reports enables large-scale training of medical imaging models.
First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0. 851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data.
Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and varying amounts of data---in particular, extremely scarce amounts of available training data.
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
2 code implementations • 10 Jun 2019 • David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
12 code implementations • 21 Jan 2019 • Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng
On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies.
Ranked #85 on Multi-Label Classification on CheXpert
With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes.
1 code implementation • • Nicholas Bien, Pranav Rajpurkar, Robyn L. Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N. Patel, Kristen W. Yeom, Katie Shpanskaya, Safwan Halabi, Evan Zucker, Gary Fanton, Derek F. Amanatullah, Christopher F. Beaulieu, Geoffrey M. Riley, Russell J. Stewart, Francis G. Blankenberg, David B. Larson, Ricky H. Jones, Curtis P. Langlotz, Andrew Y. Ng, Matthew P. Lungren
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
11 code implementations • 11 Dec 2017 • Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L. Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
45 code implementations • 14 Nov 2017 • Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Ranked #3 on Pneumonia Detection on ChestX-ray14
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor.
Data noising is an effective technique for regularizing neural network models.
Motivated by these issues, we present a neural network-based approach to language correction.
no code implementations • 7 Apr 2015 • Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, Fernando Mujica, Adam Coates, Andrew Y. Ng
We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection.
Ranked #2 on Lane Detection on Caltech Lanes Cordova
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
This approach to decoding enables first-pass speech recognition with a language model, completely unaided by the cumbersome infrastructure of HMM-based systems.
We compare standard DNNs to convolutional networks, and present the first experiments using locally-connected, untied neural networks for acoustic modeling.
Ranked #11 on Speech Recognition on swb_hub_500 WER fullSWBCH
Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images.
This work introduces a model that can recognize objects in images even if no training data is available for the objects.
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.
For example, is it possible to learn a face detector using only unlabeled images?
Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification.
In this work we focus on that component of adaptation which occurs during an organism's lifetime, and show that a number of unsupervised feature learning algorithms can account for features of normal receptive field properties across multiple primary sensory cortices.
We show that the soft reconstruction cost can also be used to prevent replicated features in tiled convolutional neural networks.
Ranked #111 on Image Classification on STL-10
Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer.
We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term–document information as well as rich sentiment content.
Using convolutional (tied) weights signiﬁcantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture.
In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks.
Using multiple regularization hyperparameters is an effective method for managing model complexity in problems where input features have varying amounts of noise.