International Business Machines Corporation

Ran Bakalo

Rami Ben-Ari

Pavel Kisilev

Dana Levanony

Ayelet Akselrod-Ballin

Jacob Goldberger

Yoni Choukroun

According to one embodiment, a method, computer system, and computer program product for hard negative training is provided. The embodiment may include a computer receiving a training set, where the training set comprises one or more training samples. The computer trains a deep neural network (DNN) with the training set. The embodiment may also include determining, using the DNN, information for each of the one or more training samples, where the information includes one or more scores associated with the one or more training samples. The embodiment may further include generating a training epoch from the one or more training samples based on the information and updates the information based on using the training epoch with the DNN.

Estimated online hard negative mining via probabilistic selection and scores history consideration

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

Embodiments may classify medical images, such as mammograms, using weakly labeled data sets, fully labeled data sets, or a combination of both. For example, a method may comprise receiving a whole medical image, extracting a plurality of image patches from the whole medical image, each image patch including a portion of the whole image, generating a representation of features found in the plurality of image patches, classifying each image patch as including a malignant abnormality, a benign abnormality or not including an abnormality to form a classification for each patch, in parallel, the detection branch computes a malignant distribution over patches and a benign distribution over patches resulting in ranking of patches compare to one another for malignancy, and ranking of patches compare to one another for benign. Patches classification probabilities and ranking are multiplied and summed for malignant and benign, resulting in global malignant probability and global benign probability.

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Haifa, Israel

Ran Bakalo