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\%.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingQuantizationTransfer LearningSimilar Papers 제목 키워드 기반
Training Stacked Denoising Autoencoders for Representation Learning
We implement stacked denoising autoencoders, a class of neural networks that are capable of learning powerful representations of high dimensional data. We describe stochastic gradient descent for unsupervised training of…
Denoisingimage-classificationImage ClassificationRepresentation LearningSemi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
The work presented explores the use of denoising autoencoders (DAE) for brain lesion detection, segmentation and false positive reduction. Stacked denoising autoencoders (SDAE) were pre-trained using a large number of un…
DenoisingLesion DetectionSegmentationTransfer LearningStacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification
Extreme learning machine (ELM), which can be viewed as a variant of Random Vector Functional Link (RVFL) network without the input-output direct connections, has been extensively used to create multi-layer (deep) neural …
Decision MakingDenoisingGeneral ClassificationRemote sensing framework for geological mapping via stacked autoencoders and clustering
Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionali…
ClusteringDimensionality ReductionLearning Representations of Affect from Speech
There has been a lot of prior work on representation learning for speech recognition applications, but not much emphasis has been given to an investigation of effective representations of affect from speech, where the pa…
DenoisingEmotion ClassificationRepresentation Learningspeech-recognition+1