Digital pathology and artificial intelligence

Abstract

In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.

Figure 1:

Example of synthetic breast cancer image generation. a) Input image with desired Ki67 positive (green) and negative nuclei (red). b) Synthetic image generated by cGAN. The nuclei location and size in the synthetic image are the same as in the input image. c) Randomly drawn lines. d) Synthetic bladder cancer image generated based on the randomly drawn lines in (c).

Figure 1:

Example of synthetic breast cancer image generation. a) Input image with desired Ki67 positive (green) and negative nuclei (red). b) Synthetic image generated by cGAN. The nuclei location and size in the synthetic image are the same as in the input image. c) Randomly drawn lines. d) Synthetic bladder cancer image generated based on the randomly drawn lines in (c).

Figure 2:

Example of color normalization. a) Reference image: T1 bladder cancer image cropped from a whole slide image. b) Image to be normalized to Reference Image. The objective is to change the colors of (b) so that it has a similar color appearance as the Reference image. c) Color normalized image produced by cGAN.

Figure 3:

Example of tumor identification from Ki67 stained slides of pancreatic neuroendocrine tumor. a) Image cropped from Ki67 slide of pancreatic neuroendocrine tumor patient. b) The non-tumor regions are automatically outlined by a deep learning algorithm.

Figure 4:

Adjacent tissue sections of colorectal cancer patient. a) H&E image. b) Adjacent tissue section of (a) stained for pan-cytokeratin to assist in the identification of tumor buds. c) H&E and pan-cytokeratin images overlaid on each other to depict the non-linear deformation between the tissue sections.