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- IBM (United States)US73 papers
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- Max Planck Institute of Microstructure PhysicsDE13 papers
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6 papers · 2 filters
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…
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
Jeffrey L. McKinstry, Steven K. Esser, Rathinakumar Appuswamy +4
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremen…
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…