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Poster
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
Marijn F Stollenga · Wonmin Byeon · Marcus Liwicki · Jürgen Schmidhuber

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #10 #None

Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelise on GPUs. Here we re-arrange the traditional cuboid order of computations in MD-LSTM in pyramidal fashion. The resulting PyraMiD-LSTM is easy to parallelise, especially for 3D data such as stacks of brain slice images. PyraMiD-LSTM achieved best known pixel-wise brain image segmentation results on MRBrainS13 (and competitive results on EM-ISBI12).

Author Information

Marijn F Stollenga (IDSIA)
Wonmin Byeon (IDSIA)
Marcus Liwicki (TU Kaiserslautern)
Jürgen Schmidhuber (IDSIA)

Since age 15, his main goal has been to build an Artificial Intelligence smarter than himself, then retire. The Deep Learning Artificial Neural Networks developed since 1991 by his research groups have revolutionised handwriting recognition, speech recognition, machine translation, image captioning, and are now available to billions of users through Google, Microsoft, IBM, Baidu, and many other companies (DeepMind also was heavily influenced by his lab). His team's Deep Learners were the first to win object detection and image segmentation contests, and achieved the world's first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition. His formal theory of fun & creativity & curiosity explains art, science, music, and humor. He has published 333 papers, earned 7 best paper/best video awards, the 2013 Helmholtz Award of the International Neural Networks Society, and the 2016 IEEE Neural Networks Pioneer Award. He is also president of NNAISENSE, which aims at building the first practical general purpose AI.

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