Distribution-Based Categorization of Classifier Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples

Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%

Classificação automática do estado de células microglia usando stacked denoising auto-encoders

Mestrado em Matemática e AplicaçõesEnquanto classe de c elulas constituinte do Sistema Nervoso Central, a microglia e respons avel pela sua manuten c~ao e defesa imunol ogica. Uma c elula desta classe pode ser encontrada em tr^es estados distintos (repouso, transi c~ao e ativo) sendo que o estado re ete o que est a a ocorrer no Sistema Nervoso Central; em particular, pode indiciar o in cio do desenvolvimento de uma doen ca neurodegenerativa. Nesta disserta c~ao, apresentamos o primeiro estudo para o reconhecimento autom atico do estado de c elulas microglia utilizando stacked denoising auto-encoders. Para obter o modelo de reconhecimento mais adequado, recorremos a diferentes estrat egias, nomeadamente, ao pr e-processamento de imagem, ao aumento arti cial do conjunto de dados (usando rota c~oes das imagens) e a resolu c~ao de sub problemas do problema original. Aplicamos tamb em transfer^encia de aprendizagem considerando cinco problemas fonte. Os resultados obtidos mostram que o estado de transi c~ao e o mais dif cil de reconhecer. Em termos de taxa de acertos, um desempenho de aproximadamente 64% e obtido.As a class of cells composing the Central Nervous System, microglia is responsible for its maintenance and immunological defense. A cell of such class may be found in three distinct states (resting, transition and active) and the state re ects what is occurring on the Central Nervous System; particularly, it may indicate the beginning of the development of a neurodegenerative disease. In this dissertation, we present the rst study for the automatic recognition of microglial cells' state using stacked denoising auto-encoders. In order to obtain the most appropriate recognition model, we resort to di erent strategies, namely, to image pre-processing, to arti cial increase of the dataset (using image rotations) and to solving sub problems of the original problem. We also apply transfer learning considering ve source problems. The obtained results show that the transition state is the most hard to recognize. In terms of accuracy, a performance of approximately 64% is achieved