Abstract: Aspects and embodiments relate to a method and apparatus configured to perform that method. The method relates to generating an indication of relevance associated with a selected biological phenomenon indicative of condition of an immune system of a patient, that selected biological phenomenon being associated with response of a patient to a chosen therapy. The method comprises steps of: determining one or more measurable input parameters characteristic of the patient and associated with the biological phenomenon; mapping the one or more input parameters to a contribution to a score associated with the selected biological phenomenon; and combining the mapped contributions across the one or more input parameters to calculate the score associated with the selected biological phenomenon.

Abstract: Disclosed is a system for analysis of microscopic image data representing a plurality of images acquired from cells. The system comprises a data processing system which is configured to read and/or generate (120) segmentation data for each of the images. For each of the images, the segmentation data are indicative of a segmentation of at least a portion of the respective image into one or more image regions so that each of the image regions is a member of one or more predefined classes of image content. The data processing system further generates co-registration data using at least portions of the segmentation data for co-registering at least portions of different ones of the images. The data processing system further generates mapping data using at least portions of the segmentation data for mapping between image regions of different images.

Abstract: Currently, there is interest in applying machine learning techniques to analyse digital pathology images automatically. Machine learning techniques often rely on training with a ground-truth image input. The quality and amount of training data determines the quality of the detector, as expressed in the rate of true and false positives, and robustness against variations in the appearance of the input images. The present application proposes to obtain image data of the same sample before and after at least one re-staining step (firstly with a structure-revealing stain, and secondly with a bio marker revealing stain). Sections of the first and second image data having a good registration relationship are chosen, along with the probability of detecting a desired candidate object (such as nucleus) and the probability of the bio marker revealing stain being present annotation data suitable for training a machine learning algorithm on the first and/or the second image data is provided.

Abstract: Disclosed is a device for controlling a volume of an analysis chamber of a micro chamber arrangement to which a region of interest of an object is exposed. The volume is controlled using a volume reducing element which is deposited on a surface of the micro chamber arrangement. The device comprises a deposition unit configured to determine a position and an extent of the volume reducing element depending on (a) the region of interest and further depending on (b) a predetermined level by which a volume of the analysis chamber is reduced using the volume reducing structure. The deposition unit is further configured to deposit the volume reducing element depending on the determined position and extent.

Abstract: The present invention relates to detecting objects in medical images. In order to provide an improved detection of objects in medical images, a medical image detection device (10) is provided that comprises an image data input (12) and a processing unit (14). The image data input is configured to receive image data of a biological sample. The processing unit comprises a detector (16) and a classifier (18). The detector is configured to detect objects of interest in the sample by a detection in the image data of at least one predetermined object feature. The detected objects being candidate objects, wherein the candidate objects comprise true positives and possible false positives. Further, the classifier is configured to classify the possible false positives as false positives or as true positives. The classifier is a trained classifier, trained specifically to recognize the false positives of the detector.