R. Summers

Publications546
h-index65
Citations19,299
Highly Influential Citations945
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ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
TLDR
A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset. Expand
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
TLDR
Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported. Expand
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
TLDR
A large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Expand
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
TLDR
This paper presents a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography CT scans, using multi-level deep convolutional networks ConvNets, and proposes and evaluates several variations of deep ConvNETS in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. Expand
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
TLDR
The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks. Expand
A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations
TLDR
This work operates a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI), and decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. Expand
DeepPap: Deep Convolutional Networks for Cervical Cell Classification
TLDR
This paper proposes a method to directly classify cervical cells—without prior segmentation—based on deep features, using convolutional neural networks (ConvNets), which outperforms previous algorithms in classification accuracy, area under the curve values, and especially specificity. Expand
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays
TLDR
A novel Text-Image Embedding network (TieNet) is proposed for extracting the distinctive image and text representations of chest X-rays and multi-level attention models are integrated into an end-to-end trainable CNN-RNN architecture for highlighting the meaningful text words and image regions. Expand
Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆
TLDR
This work localizes the pancreas from the entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step, and introduces a fully deep‐learning approach, based on an efficient application of holistically‐nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views. Expand
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
TLDR
A deep learning model is presented to efficiently detect a disease from an image and annotate its contexts (e.g., location, severity and the affected organs), and a novel approach to use the weights of the already trained pair of CNN/RNN on the domain-specific image/text dataset, to infer the joint image/ text contexts for composite image labeling. Expand
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