Cellavision Ab

Kent Stråhlén

Martin Almers

Adam Morell

Benny Klein

Gert-Ola Carlsson

Mats Erikson

Hans-Inge Bengtsson

Anna Kapferer

Lisbeth Palm

Mattias Nilson

The disclosure relates to a method for training a neural network classifier () to classify a digital image depicting one or more objects of a biological sample into a specific class, which class belongs to a predefined set of classes (C-C), the method comprising: providing a training set of digital images (-) originating 5 from a plurality of biological samples, each digital image () of the training set being labeled with a specific class (C) of the one or more objects of the digital image (), each digital image () of the training set being associated with global data () pertaining to the respective sample, training the neural network () using pixel data of each digital image () from the training set of digital 10 images (-) and the global data () associated with said digital image () as input, and using the specific class (C) of the label of said digital image () as a correct output from the neural network (). The disclosure further relates to an analyzing apparatus ().

Method and apparatus for training a neural network classifier to classify an image depicting one or more objects of a biological sample

Methods and systems for generating a graphical user interface for analysis of red blood cells. The method generates a first view of the graphical user interface by highlighting red blood cells in an image that are classified into at least one group indicated by a user input. The method also generates a second view by extracting individual red blood cells being classified into the at least one group indicated by the user input from the image of red blood cells and displaying them group-wise.

Graphical user interface for analysis of red blood cells

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Kävlinge, Sweden

Kent Stråhlén