Automated image analysis has had a long history but continues to grow with massive improvements in algorithms, speed, performance, and with emerging opportunities for high throughput tissue biomarker analysis and automated decision support for primary diagnostics. Of particular importance is the development of computer vision and image analysis for H&E stained samples. This talk will outline recent advances in automated tissue analysis for biomarker discovery and diagnostics and how adoption of digital pathology will drive the demand for quantitative imaging and decision support. As an example, PathXL have developed TissueMark for the automated identification and analysis of tumour in lung, colon, breast and prostate cancer digital H&E slides. The conventional pathological estimation of % tumour nuclei in H&E samples shows gross variation between pathologists, undermining the quality of next generation sequencing, molecular testing and patient therapy and potential of false negative diagnoses. TissueMark uses a combination of pattern recognition, glandular analysis and nuclear segmentation to identify premaligant and invasive cancer patterns in H&E stained tissues and use this to assess tumour cell numbers and annotate samples for nucleic acid extraction and molecular profiling. Benchmark data was generated to validate TissueMark technology and showed concordance of automated data with manual counts, accelerating tumour markup and improving sample quality assessment. This represents an example of how automated imaging of tissue samples can be of immense value in quantitative tumour analysis for molecular diagnostics, thereby improving reliability in discovery and diagnostics. This together with other examples in pathology research and practice will demonstrate that next generation tissue imaging technology in digital pathology could radically change how pathology is practiced.