Diffeomorphic Counterfactuals With Generative Models.

Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transformation and then perform gradient ascent in these coordinates to find counterfactuals which are classified with great confidence as a specified target class. We propose two methods to leverage generative models to construct such suitable coordinate systems that are either exactly or approximately diffeomorphic. We analyze the generation process theoretically using Riemannian differential geometry and validate the quality of the generated counterfactuals using various qualitative and quantitative measures.

CNN cascades for segmenting sparse objects in gigapixel whole slide images.

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, large amounts of digital image data are being generated. Accordingly, there is a strong demand for the development of computer based image analysis systems. Here, we address application scenarios in histopathology consisting of sparse, small objects-of-interest occurring in the large gigapixel images. To tackle the thereby arising challenges, we propose two different CNN cascade approaches which are subsequently applied to segment the glomeruli in whole slide images of the kidney and compared with conventional fully-convolutional networks. To facilitate unbiased evaluation, eight-fold cross-validation is performed and finally means and standard deviations are reported. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained (precision: 0.89, recall: 0.92). Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to previous approaches. We can state that especially one of the proposed cascade networks proved to be a highly powerful tool providing the best segmentation accuracies and also keeping the computing time at the lowest level. This work facilitates accurate automated segmentation of renal whole slide images which consequently allows fully-automated big data analyses for the assessment of medical treatments. Furthermore, this approach can also easily be adapted to other similar biomedical application scenarios.