International Business Machines Corporation

Tel Aviv University

Yoni Choukroun

Pavel Kisilev

Lior Wolf

Rami Ben-Ari

Ayelet Akselrod-Ballin

Ran Bakalo

Disclosed herein are systems and method for training neural network based decoders for decoding error correction codes, comprising obtaining a plurality of training samples comprising one or more codewords encoded using an error correction code and transmitted over a transmission channel where the training samples are subject to gradual interference over a plurality of time steps and associate the encoded codeword(s) with an interference level and a parity check syndrome at each of the plurality of time steps, using the training samples to train a neural network based decoder to decode codewords encoded using an error correction code by (1) estimating a multiplicative interference included in the encoded codeword(s) based on reverse diffusion applied to the encoded codeword(s) across the time steps, (2) computing an additive interference included in the encoded codewords based on the multiplicative interference, and (3) recovering the codeword(s) by removing the additive interference.

Decoding of error correction codes based on reverse diffusion

There is provided a method for training a deep convolutional neural network (CNN) for detecting an indication of likelihood of abnormality, comprising: receiving anatomical training images, each including an associated annotation indicative of abnormality for the whole image without an indication of location of the abnormality, executing, for each anatomical training image: decomposing the anatomical training image into patches, computing a feature representation of each patch, computing for each patch, according to the feature representation of the patch, a probability that the patch includes an indication of abnormality, setting a probability indicative of likelihood of abnormality in the anatomical image according to the maximal probability value computed for one patch, and training a deep CNN for detecting an indication of likelihood of abnormality in a target anatomical image according to the patches of the anatomical training images, the one patch, and the probability set for each respective anatomical training image.

Systems and methods for automatic detection of an indication of abnormality in an anatomical image

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Tel Aviv, Israel

Yoni Choukroun