Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort

Brain tumors are the most common solid tumors in children and young adults, but the scarcity of large histopathology datasets has limited the application of computational pathology in this group. This study implements two weakly supervised multiple-instance learning (MIL) approaches on patch-features obtained from state-of-the-art histology-specific foundation models to classify pediatric brain tumors in hematoxylin and eosin whole slide images (WSIs) from a multi-center Swedish cohort. WSIs from 540 subjects (age 8.5$\pm$4.9 years) diagnosed with brain tumor were gathered from the six Swedish university hospitals. Instance (patch)-level features were obtained from WSIs using three pre-trained feature extractors: ResNet50, UNI and CONCH. Instances were aggregated using attention-based MIL (ABMIL) or clustering-constrained attention MIL (CLAM) for patient-level classification. Models were evaluated on three classification tasks based on the hierarchical classification of pediatric brain tumors: tumor category, family and type. Model generalization was assessed by training on data from two of the centers and testing on data from four other centers. Model interpretability was evaluated through attention-mapping. The highest classification performance was achieved using UNI features and AMBIL aggregation, with Matthew's correlation coefficient of 0.86$\pm$0.04, 0.63$\pm$0.04, and 0.53$\pm$0.05, for tumor category, family and type classification, respectively. When evaluating generalization, models utilizing UNI and CONCH features outperformed those using ResNet50. However, the drop in performance from the in-site to out-of-site testing was similar across feature extractors. These results show the potential of state-of-the-art computational pathology methods in diagnosing pediatric brain tumors at different hierarchical levels with fair generalizability on a multi-center national dataset.

Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images

In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for two classification tasks using two OCT open-access datasets extensively used in the literature, Kermany's ophthalmology dataset and AIIMS breast tissue dataset. Our results show that the classification accuracy is inflated by 3.9 to 26 percentage units for models tested on a dataset with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting for research on deep learning using OCT data and volumetric data in general.

Does contextual information improve 3D U-Net based brain tumor segmentation?

Effective, robust and automatic tools for brain tumor segmentation are needed for extraction of information useful in treatment planning. In recent years, convolutional neural networks have shown state-of-the-art performance in the identification of tumor regions in magnetic resonance (MR) images. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy. In this work it is instead investigated if the addition of contextual information in the form of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) masks improves U-Net based brain tumor segmentation. The BraTS 2020 dataset was used to train and test a standard 3D U-Net model that, in addition to the conventional MR image modalities, used the contextual information as extra channels. For comparison, a baseline model that only used the conventional MR image modalities was also trained. Dice scores of 80.76 and 79.58 were obtained for the baseline and the contextual information models, respectively. Results show that there is no statistically significant difference when comparing Dice scores of the two models on the test dataset p > 0.5. In conclusion, there is no improvement in segmentation performance when using contextual information as extra channels.