From Grain to Insight: Explainability in AI for Biological Data Analysis

In the field of machine learning, a common focus is on benchmark datasets, which are standardized sets of data used to compare the performance of different methods. However, this thesis emphasizes the importance of considering real-world applications, specifically, the detection of diseases and damages in grain kernels using image data. This application presents unique challenges due to the biological variation inherent in the data, which differs significantly from standard benchmark datasets.

The primary objective of this research is to enhance disease detection capabilities and incorporate explainability into the process, making it more transparent and understandable. To achieve this, we investigate the use of knowledge graphs, a tool that can leverage existing metadata to improve image classification and generate specific data collections.

One of the key challenges we address is the application of post-hoc explainability methods to data with biological variation. We propose a workflow to guide the selection of the most appropriate method for any given application. Furthermore, we assess the resilience of these methods to minor, naturally occurring variations in input images.

Lastly, we explore concept-based explainability and alignment of representations in machine learning models. We aim to make these models more understandable by relating their operations to high-level concepts. We also strive to align the model’s perception and processing of information with human cognitive processes. This exploration could provide valuable insights into how machine learning models can be more effectively aligned with human understanding.