1.1k citations · 1.8k across the 32 of their papers we have counts for
13 papers · 1 filter
Medical visual question answering using joint self-supervised learning
Yuan Zhou, Jing Mei, Yiqin Yu +1
Visual Question Answering (VQA) becomes one of the most active research problems in the medical imaging domain. A well-known VQA challenge is the intrinsic diversity between the im…
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Tanveer Syeda-Mahmood, Ph. D, K. C. L Wong +6
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray d…
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Satyananda Kashyap, Alexandros Karargyris, Joy Wu +5
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses…
Chest X-ray Report Generation through Fine-Grained Label Learning
Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur +9
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the…
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Tanveer Syeda-Mahmood, Hassan M. Ahmad, Nadeem Ansari +9
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals. There has been a surge of work on automatic interpretation of chest X-rays using deep learning a…
Identifying disease-free chest X-ray images with deep transfer learning
Ken C. L. Wong, Mehdi Moradi, Joy Wu +1
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They are mostly used for screening, and an indication of disease typically results in subsequent test…
Age prediction using a large chest X-ray dataset
Alexandros Karargyris, Satyananda Kashyap, Joy T Wu +3
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a person…
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
Ken C. L. Wong, Mehdi Moradi, Hui Tang +1
With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networ…
Bimodal network architectures for automatic generation of image annotation from text
Mehdi Moradi, Ali Madani, Yaniv Gur +2
Medical image analysis practitioners have embraced big data methodologies. This has created a need for large annotated datasets. The source of big data is typically large image col…
Building medical image classifiers with very limited data using segmentation networks
Ken C. L. Wong, Tanveer Syeda-Mahmood, Mehdi Moradi
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with…
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood +1
Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive…
Domain-adversarial neural networks to address the appearance variability of histopathology images
Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof +2
Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab…