Master's Thesis presentation, Filip Dahlén

Supervisor and examiner: Larisa Beilina

Passcode 392926

Abstract: Accurate prediction of tumor metastasis is crucial for cancer prognosis and treatment. This study explores the application of artificial intelligence techniques, specifically neural networks and Principal Component Analysis (PCA), for predicting tumor metastasis based on histopathological images. The aim is to identify an effective approach that balances accuracy and computational efficiency for Metastatic Tumor Prediction.

Metastatic prediction is addressed using Whole Slide Images (WSI) obtained from Sahlgrenska University Hospital. Preprocessing involves dividing WSIs into smaller tiles and extracting relevant features such as breslow depth and metastatic status. Two models are utilized: a modified ResNet18 and a shallower network (ConvNet2/ConvNet3) with varying inputs. Training and evaluation are performed using a 4-fold cross-validation approach and mini-batches.  The results indicate that the PCA algorithm performed similar to the neural networks but was outperformed in terms of computational time. The combined model showcased the potential in reducing computational time while maintaining accuracy when the there are a large amount of available with high complexity. Lastly, the study showed that when the data are limited the best approach was to utilize a neural network based on image data and clinical data to enhance metastatic prediction. Overall, this thesis emphasizes the potential of neural networks based on image and clinical data for enhancing metastatic prediction, providing valuable insights for improved cancer prognosis and treatment decision-making.