… Incorporating such knowledge into deep learning framework could improve the diagnostic
accuracy. This is called multimodal deep learning. Since dimension of the image data is …

… diagnostic pathology. This paper reviews the different approaches to deep learning in pathology,
the … and a range of emerging applications in pathology. The translation of AI into clinical …

… unimodal approaches in classifying radiology and pathology images. Moreover, when …
pathology assessments such as GS and GG. While the training combines MRI data with pathology …

… molecular biology assays, advances in deep learning (DL) are facilitating the … diagnostic
tasks, DL has shown potential to be useful to automate repetitive tasks in diagnostic pathology…

… Although inter- and intra-observer experience variability exists in the pathological diagnosis27,
28, our results demonstrate that deep learning assistance leads to more reliable and …

… The deep learning (DL)-based approaches in tumor pathology help to overcome the limitations
of subjective visual examination from pathologists and improve diagnostic accuracy and …

… We have thus applied deep learning to … of deep learning into the pathologic diagnosis of
NPC based on WSIs, ii) reduction of the dependence of personal experience in the diagnostic …

… results in using Deep Learning to detect malignancy in … Deep Learning with a convolutional
neural network (CNN) algorithm to build a lymphoma diagnostic model for four diagnostic …

… To evaluate the potential impact of our algorithm on deep learning–supported diagnosis,
we conducted a multicenter, reader-blinded study for classification of gastric biopsies. Three …

… In conclusion, we developed deep learning models to diagnose GC and predict the survival
outcomes of GC patients by analyzing H&E-stained pathological images. To make our …