Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability

Explaining Machine Learning Models by Generating Counterfactuals

Nowadays, machine learning is being applied in various domains, including safety critical areas, which directly affect our lives. These systems are so complex and rely on huge amounts of training data, so that we risk to create systems that we do not understand, which might lead to undesired behavior, such as fatal decisions, discrimination, ethnic bias, racism and others. Moreover, European Union recently adopted General Data Protection Regulation (GDPR), which requires companies to provide meaningful explanation of the logic behind decisions made by machine learning systems, if these decisions affect directly a human being. We address the issue of explaining various machine-learning models by generating counterfactuals for given data points. Counterfactual is a transformation, which shows how to alternate an input object, so that a classifier predicts a different class. Counterfactuals allow us to better understand why particular classification decisions take place. They may aid in troubleshooting a classifier and identifying biases by looking at alternations needed to be made in the data instances. For example, if a loan approval application system denies a loan for a particular person, and we can find a counterfactual indicating that we need to change the gender, or the race of a person for the loan to be approved, then we have identified bias in the model and we need to study our classifier better and retrain it to avoid such undesired behavior. In this thesis we propose a new framework to generate counterfactuals for a set of data points. The proposed framework aims to find a set of similar transformations to data points, such that those changes significantly reduce the probabilities of the target class. We argue that finding similar transformations for a set of data points helps to achieve more robust explanations to classifiers. We demonstrate our framework on 3 types of data: tabular, images and texts. We evaluate our model on both simple and real-world datasets, including ImageNet and 20 NewsGroups

Biometric Fish Classification of Nordic Species Using Convolutional Neural Network with Squeeze-and-Excitation

Master's thesis Information- and communication technology IKT590 - University of Agder 2018Squeeze-and-Excitation (SE) is a technique within convolutional neural networks (CNN) that can be applied to existing CNNs by applying fullyconnected layers between convolutional layers and merging the outputs. SE was the winning architecture of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2017. In this thesis, we propose a CNN using the SE architecture for classifying images of sh. Previous work in the eld relies on applying lters to the images to separate the sh from the background or sharpen the images by removing background noise. The images from the dataset are extracted from underwater cameras and contain noise, which is why classifying these images is challenging. Di erent from conventional schemes, this approach is divided into two classi cation problems. The rst approach is to classify sh from the Fish4Knowledge dataset without using image augmentation, and the second is to classify sh from a new dataset consisting of Nordic species. We name the rst approach pre-training, and the second post-training. The weights from pre-training are applied to post-training. Our solution achieves the state-of-the-art accuracy of 99.27% accuracy on the pre-training. The accuracy on the post-training is lower with an accuracy of 83.68%. Experiments on the post-training with image augmentation yields an accuracy of 87.74%, indicating that the solution is viable with a larger dataset. Keywords: Classi cation, CNN, Squeeze-and-Excitatio