The present disclosure relates to techniques for segmenting and detecting cells within image data using transfer learning and a multi-task scheduler. Particularly, aspects of the present disclosure are directed to accessing a plurality of images of one or more cells, extracting three labels from the plurality of images, where the three labels are extracted using a Voronoi transformation, a local clustering, and application of repel code, training, by a multi-task scheduler, a convolutional neural network model based on three loss functions corresponding to the three labels, generating, by the convolutional neural network model, a nuclei probability map and a background probability map for each of the plurality of images based on the training with the three loss functions, and providing the nuclei probability map and the background probability map.
WEAKLY SUPERVISED MULTI-TASK LEARNING FOR CELL DETECTION AND SEGMENTATION