15.4k citations · 17.1k across the 7 of their papers we have counts for
8 papers
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Sebastian Gündel, Arnaud A. A. Setio, Florin C. Ghesu +4
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of da…
Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation
Sebastian Guendel, Arnaud Arindra Adiyoso Setio, Sasa Grbic +2
Chest X-ray (CXR) is the most common examination for fast detection of pulmonary abnormalities. Recently, automated algorithms have been developed to classify multiple diseases and…
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks
Siqi Liu, Arnaud Arindra Adiyoso Setio, Florin C. Ghesu +4
Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniqu…
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Jie Yang, Siqi Liu, Sasa Grbic +7
Though large-scale datasets are essential for training deep learning systems, it is expensive to scale up the collection of medical imaging datasets. Synthesizing the objects of in…
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
Siqi Liu, Eli Gibson, Sasa Grbic +5
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations. Such systems require data to be annotated by experts with years…
A Survey on Deep Learning in Medical Image Analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi +6
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning…