International Business Machines Corporation

Alon Hazan

Vadim Ratner

Aviad Zlotnick

Ella Barkan

Murray A Reicher

Yoel Shoshan

Daniel Khapun

Flora Gilboa

Embodiments of the present systems and methods may provide techniques that are applicable to a variety of imaging modalities and that utilize prior knowledge of the dynamics of a physiological system to analytically generate augmented samples for machine learning. For example, in an embodiment, a method implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise generating a transform for an image of tissue based on deformation of the tissue under compression, obtaining an image of tissue using an imaging modality, and generating an output image by transforming the image of the tissue using the transform.

Methods to induce non-rigid mesh transformation in-variance to CNN based medical imaging classifiers

A method comprising using at least one hardware processor for automatically receiving, using a user interface associated with the hardware processor(s), an annotation for a medical image of a patient, the medical image associated with a suspected disease of the patient and metadata of the patient. The hardware processor(s) are configured for performing a search query in real time on a database for the annotation. The hardware processor(s) are configured for receiving at least one result of the search query. The hardware processor(s) are configured for retrieving at least one other medical image comprising at least one other annotation, wherein the at least one other medical image is associated with the at least one result. The hardware processor(s) are configured for displaying, on the user interface, the at least one other medical image and the at least one other annotation.

Reduce discrepancy of human annotators in medical imaging by automatic visual comparison to similar cases

In some examples, a system for training a neural network can include a processor to detect a trained neural network application. The processor can also detect a set of images, wherein the neural network application is not trained with the set of images. Additionally, the processor can train an adapter network based on the trained neural network application and the set of images, wherein the adapter network is to be trained by freezing weights of the trained neural network and modifying weights of the adapter network. Furthermore, the processor can use the trained adapter network to process at least one additional image, the processed additional image to be transmitted to the trained neural network to generate an output signal.

Training a neural network adapter

Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes generating an interactive classification user interface concurrently displaying a first group of medical images and a second group of medical images, each group depicting objects associated with a respective classification. User input indicating movement of medical images from the first group to the second group is detected. The moved medical images are classified according to the second group. The re-classified medical images are provided to a machine learning system, with the machine learning system updating based on analysis of object characteristics of the re-classified medical images to increase accuracies associated with automated assignment of classifications.

Systems and user interfaces for enhancement of data utilized in machine-learning based medical image review

A method comprising using at least one hardware processor for: receiving a digital medical image and a binary image mask, wherein the binary image mask depicts a segmentation of a lesion in the digital medical image; computing a plurality of layers of the lesion; for each of the plurality of layers of the lesion, extracting layer features; and sending the extracted layer features to a lesion classifier.

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Zikhron Yaakov, Israel

Alon Hazan