Deep learning (DL) is a subfield of artificial intelligence (AI) focused on developing algorithms that learn from data to perform some tasks that can aid humans in their daily life or work assignments. Research demonstrates the potential of DL in supporting pathologists with routine tasks like detecting breast cancer metastases and grading prostate cancer. However, a widespread adoption of DL technology in pathology labs has been slow for several reasons. DL models often exhibit performance variations across medical centres, patient subgroups, and even within the same centre over time. While collecting more data and retraining the algorithms seems like a straightforward solution, it is a costly and time-consuming process. Moreover, retraining DL systems with regulatory approvals is complex due to existing regulations. Another limitation of DL models is their inability to provide confidence estimates for predictions, leaving users in the dark about their reliability. Finally, establishing a close collaboration between the research community, vendors, and pathology labs is crucial for producing effective DL systems for patient care. However, this collaboration faces challenges like miscommunication, misalignment of goals, and misunderstanding priorities.

This thesis presents various approaches that could tackle the generalisation and reliability challenges faced by diagnostic DL systems for digital pathology with a strong emphasis on the clinical needs. To address the generalisation issues, an unsupervised approach to quantify expected changes in a model’s performance between two datasets is proposed. This approach can serve as an initial validation step before deploying diagnostic DL systems in clinical practice, reducing annotation costs. Additionally, an unsupervised framework based on generative models is proposed to identify substantially different inputs, known as out-of-distribution (OOD) samples. Detecting OOD samples plays a crucial role in enhancing the reliability of DL algorithms. Furthermore, several studies are conducted to explore what benefits uncertainty estimation could bring. Firstly, various uncertainty estimation approaches are extensively evaluated, focusing on identifying incorrect predictions and generalisability issues between medical centres and specific patient groups. In addition, the results reveal that combining uncertainty estimation methods with DL outputs leads to a more robust classification score, enhancing the overall performance and reliability of the classification process. Another study demonstrates that spatial uncertainty aggregation improves the effectiveness of uncertainty estimation in tumour segmentation tasks. This is evaluated on the detection of false negatives which may reduce the risk of missing tumour cells. Finally, the clinical prerequisites for developing and validating diagnostic DL systems for digital pathology are discussed, along with an overview of explainable AI techniques.

In conclusion, multiple approaches to facilitate the adoption of DL systems in clinical practice, addressing reliability, generalisability, and clinical needs aspects are discussed in this thesis. I believe that the extensive efforts in the research community will have a positive impact on the development, validation, and deployment of DL systems in digital pathology labs, empowering pathologists with trustworthy AI tools.