On Open Problems for Deep Learning in Biomedical Image Analysis

Deep Learning has thoroughly transformed the field of computer vision within the past years. Many standard problems such as image restoration, segmentation or registration, that were based on quite different modelling and optimisation approaches (e.g. PDEs, Markov Random Fields, Random Forests, ...), can now be solved within the framework of Deep Neural Networks with astonishing accuracy and speed (at prediction time). One important advantage of Deep Learning is its ability to capture the often complex statistical dependencies in image data and leverage this information for improving prediction, regression, or classification, given enough annotated data.

However, in the biomedical domain, one major limitation is the scarceness of suitably annotated data that rules out a lot of solutions from the computer vision domain. Here, approaches such as manifold learning, generative models, or using deep networks as structural priors are promising directions for weakly supervised or unsupervised learning in biomedical imaging. Another important aspect, in particular for medical image analysis is the interpretability (or the lack thereof) of the fitted model.

In this talk I am going to present a selection of problems in biomedical image analysis, that would greatly benefit from Deep Learning approaches, but lack the typically required amount of annotated data. I will focus on examples from high-resolution in-vivo MRI imaging of brain structure, microscopic analysis of anatomical microstructure of the human cortex and large-scale live microscopy for stem cell biology and developmental biology.