Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnoses, and 46 scans with diagnoses independently provided by a group of histopathologists can be found at https://github.com/michalkoziarski/DiagSet. Furthermore, we propose a machine learning framework for detection of cancerous tissue regions and prediction of scan-level diagnosis, utilizing thresholding to abstain from the decision in uncertain cases. The proposed approach, composed of ensembles of deep neural networks operating on the histopathological scans at different scales, achieves 94.6% accuracy in patch-level recognition and is compared in a scan-level diagnosis with 9 human histopathologists showing high statistical agreement.

Double staining in histopathology is done to help identify tissue features and cell types differentiated between two tissue samples using two different dyes. In the case of metaplastic breast cancer, H&E and P63 are often used in conjunction for diagnosis. However, P63 tends to damage the tissue and is prohibitively expensive, motivating the development of virtual staining methods, or methods of using artificial intelligence in computer vision for diagnostic strain transformation. In this work, we present results of the new xAI-CycleGAN architecture's capability to transform from H&E pathology stain to the P63 pathology stain on samples of breast tissue with presence of metaplastic cancer. The architecture is based on Mask CycleGAN and explainability-enhanced training, and further enhanced by structure-preserving features, and the ability to edit the output to further bring generated samples to ground truth images. We demonstrate its ability to preserve structure well and produce superior quality images, and demonstrate the ability to use output editing to approach real images, and opening the doors for further tuning frameworks to perfect the model using the editing approach. Additionally, we present the results of a survey conducted with histopathologists, evaluating the realism of the generated images through a pairwise comparison task, where we demonstrate the approach produced high quality images that sometimes are indistinguishable from ground truth, and overall our model outputs get a high realism rating.

Renal transplantation is widely regarded as the best treatment for patients with end-stage kidney disease. Over the past 15 years, demand in the UK for kidney transplants has been rising, resulting in more elderly deceased donors being considered. The problem with elderly donors is that kidney function deteriorates with age. Kidney transplants from elderly donors are associated with higher risks of early failure. Early failure of a kidney graft is a disastrous outcome for the recipient.

More women in India die from cervical cancer than in any other country. This preventable disease kills around 67,000 women in India every year, more than 25% of the 260,000 deaths worldwide. Effective screening and early detection can help reduce its incidence, but part of the challenge -- and there are several parts -- today is that the testing process to detect the onset of the disease is unbearably time-consuming. This is because the existing methodology that cytopathologists use is time consuming to begin with, but also because there are very few of them in the nation. Could AI speed this up?