Nilgün Şengöz

As AI systems become more prevalent in medical diagnosis, the need for transparency and interpretability grows exponentially. In my research, I focus on Explainable AI (XAI) methods that help clinicians understand how algorithms reach their decisions.

Gradient-weighted Class Activation Mapping (Grad-CAM) is one of the key techniques I use to visualize which parts of a histopathological image the model focuses on when making a diagnosis. This visual feedback is crucial for building trust between AI systems and healthcare professionals.

The challenge is not just building accurate models — it's building models that doctors can understand, trust, and ultimately use to improve patient outcomes. This is where XAI bridges the gap between algorithmic power and clinical practice.

In our recent study on paratuberculosis diagnosis, we demonstrated that Grad-CAM heatmaps closely aligned with the regions pathologists identified as diagnostically relevant, validating the model's reasoning process.

Starting a postdoctoral journey in a new country is both exciting and challenging. Nottingham has welcomed me with its beautiful green campus, friendly people, and a vibrant research community.

The University of Nottingham offers incredible resources for researchers. The labs are well-equipped, the library is extensive, and there are countless seminars and workshops to attend.

Being part of an international research community has already broadened my perspective on AI applications. The cultural exchange, the academic environment, and the opportunity to collaborate with researchers from diverse backgrounds make this journey truly rewarding.

Living in the UK as a TÜBİTAK 2219 fellow is a unique experience that I'm grateful for every day. I look forward to sharing more about my adventures in Nottingham and beyond!

Achieving high accuracy in image classification often exceeds what a single model can deliver. That's why I've been exploring hybrid approaches that combine the feature extraction power of deep learning with the classification strength of XGBoost.

Deep learning models like VGG16 and ResNet are excellent at extracting complex visual features from images. However, feeding these features into XGBoost can further enhance classification performance beyond what either method achieves alone.

In our recent work on rotten fruit detection and histopathology classification, this hybrid approach consistently outperformed standalone models, achieving accuracy rates above 99% in several benchmarks.

The key insight is that deep learning excels at representation learning while gradient boosting excels at decision boundaries. Combining both gives us the best of both worlds.