Multi-task multi-domain learning for digital staining and classification of leukocytes

oking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation …

Learn to Recognize

Detection, Classification, Segmentation, Anomaly Detection, Semi-/Weakly-Supervised Learning

Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency

Stain virtualization is an application with growing interest in digital pathology allowing simulation of stained tissue images thus saving lab and tissue resources. Thanks to the success of Generative Adversarial Networks (GANs) and the progress of …

Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically …

Staingan: Stain style transfer for digital histological images

Digitized Histological diagnosis is in increasing demand. However, color variations due to various factors are imposing obstacles to the diagnosis process. The problem of stain color variations is a well-defined problem with many proposed solutions. …

Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many …

Invited Talk: Can Deep Learning Models be Trained with Annotations Collected via Crowdsourcing?

One of the major challenges facing researchers nowadays in applying deep learning (DL) models to Medical Image Analysis is the limited amount of annotated data. Collecting such ground-truth annotations requires domain knowledge (expertise), cost, and time, making it infeasible for large-scale databases. We presented a novel concept for training DL models from noisy annotations collected through crowdsourcing platforms, i.e., Amazon Mechanical Turk, Crowdflower, by introducing a robust aggregation layer to the convolutional neural networks. Our proposed method was validated on a publicly available database on Breast Cancer Histology Images showing interesting results of our robust aggregation method compared to baseline methods, i.e., Majority Voting. In follow-up work, we introduced a novel concept of an image to game-object translation in biomedical Imaging allowing medical images to be represented as star-shaped objects that can be easily embedded to readily available game canvas. The proposed method reduces the necessity of domain knowledge for annotations. Exciting and promising results were reported compared to the conventional crowdsourcing platforms.

Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images