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公开(公告)号：US20230028525A1
公开(公告)日：2023-01-26
申请号：US17813194
申请日：2022-07-18
Applicant: Roche Diagnostics Operations, Inc.
Inventor： Nils Bruenggel , Patrick Conway , Pascal Vallotton
Abstract: A computer-implemented method of generating training data to be used to train a machine learning model for generating a segmentation mask of an image containing overlapping particles. Training data is generated from sparse particle images which contain no overlaps. Generating masks for non-overlapping particles is generally not a problem if the particles can be identified clearly; in many cases simple methods such as thresholding already yield usable masks. The sparse images can then be combined to images which contain artificial overlaps. The same can be done for the masks as well which yields a large amount of training data, because of the many combinations which can be created from just a small set of images. The method is simple yet effective and can be adapted to many domains for example by adding style-transfer to the generated images or by including additional augmentation steps.
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公开(公告)号：US20240135736A1
公开(公告)日：2024-04-25
申请号：US18485600
申请日：2023-10-12
Applicant: Roche Diagnostics Operations, Inc.
Inventor： Nils Bruenggel , Patrick Conway , Jan-Gerrit Hoogendijk , Pascal Vallotton
CPC classification number: G06V20/698 , G06T7/0012 , G06T11/206 , G06V10/774 , G06V10/82 , G16B15/00 , G16B40/20 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024 , G06T2210/41 , G06V2201/03
Abstract: A clinical support system comprises a processor and a display component, wherein: the processor is configured to: receive image data, the image data representing an image of a plurality of cells obtained from a human or animal subject, the image data comprising a plurality of subsets of image data, each subset comprising data representing a portion of the image data corresponding to a respective cell of the plurality of cells; apply a trained deep learning neural network model to each subset of the image data, the deep learning neural network model comprising: a plurality of convolutional neural network layers each comprising a plurality of nodes; and a bottleneck layer comprising no more than ten nodes, wherein the processor is configured to apply the trained deep learning neural network model to each subset of the image data by applying the plurality of CNN layers, and subsequently applying the bottleneck layer, each node of the bottleneck layer of the machine-learning model configured to output a respective activation value for that subset of the image data; for each subset of the image data, derive a dataset comprising no more than three values, the values derived from the activation values of the nodes in the bottleneck layer; and generate instructions, which when executed by the display component of a clinical support system, cause the display component of the computer to display a plot in no more than three dimensions of the respective dataset of each subset of the image data. Associated computer-implemented methods, including for training the deep learning neural network model, are provided.
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公开(公告)号：US20250046455A1
公开(公告)日：2025-02-06
申请号：US18723851
申请日：2022-12-23
Applicant: Roche Diagnostics Operations, Inc.
Inventor： Nils Bruenggel , Patrick Conway , Simon John Davidson , Emilie Dejean , Jacob Gildenblat , Chen Sagiv , Pascal Vallotton
Abstract: A computer-implemented method of differentiating between lymphoid blast cells and myeloid blast cells comprises: receiving a digital image containing one or more blast cells; applying a parametric model classifier to one or more portions of the digital image each containing a respective blast cell, the parametric model configured to generate an output indicative of whether each blast cell is a lymphoid blast cell or a myeloid blast cell. Computer-implemented methods of training a parametric model are also provided, as well as a clinical decision support system relying on the computer-implemented method of classifying blast cells.
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