Whole slide imaging is routinely adopted for carcinoma diagnosis and prognosis. Abundant experience is required for pathologists to achieve accurate and reliable diagnostic results of whole slide images (WSI). The huge size and heterogeneous features of WSIs make the workflow of pathological reading extremely time-consuming. In this paper, we propose a novel framework (WSI-VQA) to interpret WSIs by generative visual question answering. WSI-VQA shows universality by reframing various kinds of slide-level tasks in a question-answering pattern, in which pathologists can achieve immunohistochemical grading, survival prediction, and tumor subtyping following human-machine interaction. Furthermore, we establish a WSI-VQA dataset which contains 8672 slide-level question-answering pairs with 977 WSIs. Besides the ability to deal with different slide-level tasks, our generative model which is named Wsi2Text Transformer (W2T) outperforms existing discriminative models in medical correctness, which reveals the potential of our model to be applied in the clinical scenario. Additionally, we also visualize the co-attention mapping between word embeddings and WSIs as an intuitive explanation for diagnostic results.

If you use Windows, you want PowerToys. This collection of open-source goodies, guided and published by Microsoft itself, is one of the best free software packages out there, and we can't recommend it enough. That only becomes more true today, as the company publishes an updated version with three brand new tools: the previously-spotted Text Extrator (an Optical Character Recognition tool), a ruler for measuring pixels on your screen, and a tool for quickly inserting little-used accents into text. Text Extractor is probably the most universally-applicable addition here. It's an open-source version of Joseph Finney's paid Text Grab app, now integrated into PowerToys and free for Windows users.