Cancer detection in histopathology whole-slide images using conditional random fields on deep embedded spaces

Advanced image analysis can lead to automated examination to histopatholgy images which is essential for ob-jective and fast cancer diagnosis. Recently deep learning methods, in particular Convolutional Neural Networks (CNNs), have shown exceptionally successful performance on medical image analysis as well as computational histopathology. Because Whole-Slide Images (WSIs) have a very large size, the CNN models are commonly applied to classify WSIs per patch. Although a CNN is trained on a large part of the input space, the spatial dependencies between patches are ignored and the inference is performed only on appearance of the individual patches. Therefore, prediction on the neighboring regions can be inconsistent. In this paper, we apply Con-ditional Random Fields (CRFs) over latent spaces of a trained deep CNN in order to jointly assign labels to the patches. In our approach, extracted compact features from intermediate layers of a CNN are considered as observations in a fully-connected CRF model. This leads to performing inference on a wider context rather than appearance of individual patches. Experiments show an improvement of approximately 3.9% on average FROC score for tumorous region detection in histopathology WSIs. Our proposed model, trained on the Camelyon171 ISBI challenge dataset, won the 2nd place with a kappa score of 0.8759 in patient-level pathologic lymph node classification for breast cancer detection

WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need

We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue. The sTIL% score is a prognostic and predictive biomarker for many solid tumor types. However, due to the high labeling efforts and high intra- and interobserver variability within and between expert annotators, this biomarker is currently not used in routine clinical decision making. WeakSTIL compresses tiles of a WSI using a feature extractor pre-trained with self-supervised learning on unlabeled histopathology data and learns to predict precise sTIL% scores for each tile in the tumor bed by using a multiple instance learning regressor that only requires a weak WSI-level label. By requiring only a weak label, we overcome the large annotation efforts required to train currently existing TIL detection methods. We show that WeakSTIL is at least as good as other TIL detection methods when predicting the WSI-level sTIL% score, reaching a coefficient of determination of 0.45 ± 0.15 when compared to scores generated by an expert pathologist, and an AUC of 0.89 ± 0.05 when treating it as the clinically interesting sTIL-high vs sTIL-low classification task. Additionally, we show that the intermediate tile-level predictions of WeakSTIL are highly interpretable, which suggests that WeakSTIL pays attention to latent features related to the number of TILs and the tissue type. In the future, WeakSTIL may be used to provide consistent and interpretable sTIL% predictions to stratify breast cancer patients into targeted therapy arms

The development of a new multi-faceted model of social wellbeing: does income level make a difference?

Recent research has suggested that income, while playing a part in quality of life, may have only a limited impact on a multi-faceted concept such as social wellbeing. Using data from an Australian household survey (Living in Queensland Survey), a composite Wellbeing Index was created that covered objective circumstances, with known associations to wellbeing, evaluated from the individual’s subjective viewpoint. The importance attributed to each dimension added to the robustness of the measure. The measure was then used to explore the impact of income on wellbeing using various specifications of income. The results indicate that while income is a statistically significant predictor, its effect on wellbeing is small compared with other socio-demographic variables such as health, marital status, employment status and age. The study contributes to the contemporary debate on social wellbeing and adds new evidence to a body of research that has been mainly based on European and American data