Browsing by Subject "convolutional neural network"
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(2020)Immunohistochemistry (IHC) is a widely used research tool for detecting antigens and can be used in medical and biochemical research. The co-localization of two separate proteins is sometimes crucial for analysis, requiring a double staining. This comes with a number of challenges since staining results depend on the pre-treatment of samples, host-species where the antibody was raised and spectral differentiation of the two proteins. In this study, the proteins GABAR-α2 and CAMKII were stained simultaneously to study the expression of the GABA receptor in hippocampal pyramidal cells. This was performed in PGC-1α transgenic mice, possibly expressing GABAR-α2 excessively compared to wildtype mice. Staining optimization was performed regarding primary and secondary antibody concentration, section thickness, antigen retrieval and detergent. Double staining was performed successfully and proteins of interest were visualized using a confocal microscope after which image analyses were performed using two different methods: 1) a traditional image analysis based on intensity and density of stained dots and 2) a novel convolutional neural network (CNN) machine learning approach. The traditional image analysis did not detect any differences in the stained brain slices, whereas the CNN model showed an accuracy of 72% in categorizing the images correctly as transgenic/wildtype brain slices. The results from the CNN model imply that GABAR-α2 is expressed differently in PGC-1α transgenic mice, which might impact other factors such as behaviour and learning. This protocol and the novel method of using CNN as an image analysis tool can be of future help when performing IHC analysis on brain neuronal studies.
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(2022)Two factors define the success of a deep neural network (DNN) based application; the training data and the model. Nowadays, many state-of-the-art DNN models are available free of charge, and training and deploying these models is easier than ever before. As a result, anyone can set up a state-of-the-art DNN algorithm within days or even hours. In the past, most of the focus has been given to the model when researchers were building faster and more accurate deep learning architectures. These research groups commonly use large and high-quality datasets in their work, which is not the case when one wants to train a new model for a specific use case. Training a DNN algorithm for a specific task requires collecting a vast amount of unlabelled data and then labeling the training data. To train a high-performance model, the labeled training dataset must be large and diverse to cover all relevant scenarios of the intended use case. This thesis will present an efficient and straightforward active learning method to sample the most informative images to train a powerful anchor-free Intersection over Union (IoU) predicting objector detector. Our method only uses classification confidences and IoU predictions to estimate the image informativeness. By collecting the most informative images, we can cover the whole diversity of the images with fewer human-annotated training images. This will save time and resources, as we avoid labeling images that would not be beneficial.
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