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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.
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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.
View on IEEE
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.
View PDF on arXiv
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.
View PDF on arXiv
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.
View on IEEE
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.
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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.
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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.
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Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆
View on Wolters Kluwer
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
TLDR
Using DeepLesion, a universal lesion detector is trained that can find all types of lesions with one unified framework and achieves a sensitivity of 81.1% with five false positives per image.
View on SPIE
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