A hybrid approach for stain normalisation in digital histopathological images

Stain in-homogeneity adversely affects segmentation and quantifi-cation of tissues in histology images. Stain normalisation techniques have been used to standardise the appearance of images. However, most the available stain normalisation techniques only work on a particular kind of stain images. In addition, some of these techniques fail to utilise both the spatial and tex-tural information in histology images, leading to image tissue distortion. In this paper, a hybrid approach has been developed, based on an octree colour quantisation algorithm combined with the Beer-Lambert law, a modified blind source separation algorithm, and a modified colour transfer approach. The hybrid method consists of two stages the stain separation stage and colour transfer stage. An octree colour quantisation algorithm combined with Beer-Lambert law, and a modified blind source separation algorithm are used during the stain separation stage to computationally estimate the amount of stain in an histology image based on its chromatic and luminous response. A modified colour transfer algorithm is used during the colour transfer stage to minimise the effect of varying staining and illumination. The hybrid method addresses the colour variation problem in both H&DAB (Haemotoxylin and Diaminoben-zidine) and H&E (Haemotoxylin and Eosin) stain images. The stain normali-sation method is validated against ground truth data. It is widely known that the Beer-Lambert law applies to only stains (such as haematoxylin, eosin) that absorb light. We demonstrate that the Beer-Lambert law applies is applicable to images containing a DAB stain. Better stain normalisation results are obtained in both H&E and H&DAB images

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Deep Learning for Aerial Scene Understanding in High Resolution Remote Sensing Imagery from the Lab to the Wild

Diese Arbeit präsentiert die Anwendung von Deep Learning beim Verständnis von Luftszenen, z. B. Luftszenenerkennung, Multi-Label-Objektklassifizierung und semantische Segmentierung. Abgesehen vom Training tiefer Netzwerke unter Laborbedingungen bietet diese Arbeit auch Lernstrategien für praktische Szenarien, z. B. werden Daten ohne Einschränkungen gesammelt oder Annotationen sind knapp

Learning representations for supervised information fusion using tensor decompositions and deep learning methods

Machine learning is aimed at the automatic extraction of semantic-level information from potentially raw and unstructured data. A key challenge in building intelligent systems lies in the ability to extract and fuse information from multiple sources. In the present thesis, this challenge is addressed by using representation learning, which has been one of the most important innovations in machine learning in the last decade. Representation learning is the basis for modern approaches to natural language processing and artificial neural networks, in particular deep learning, which includes popular models such as convolutional neural networks (CNN) and recurrent neural networks (RNN). It has also been shown that many approaches to tensor decomposition and multi-way models can also be related to representation learning. Tensor decompositions have been applied to a variety of tasks, e.g., knowledge graph modeling and electroencephalography (EEG) data analysis. In this thesis, we focus on machine learning models based on recent representation learning techniques, which can combine information from multiple channels by exploiting their inherent multi-channel data structure. This thesis is divided into three main sections. In the first section, we describe a neural network architecture for fusing multi-channel representations. Additionally, we propose a self-attention mechanism that dynamically weights learned representations from various channels based on the system context. We apply this method to the modeling of distributed sensor networks and demonstrate the effectiveness of our model on three real-world sensor network datasets. In the second section, we examine how tensor factorization models can be applied to modeling relationships between multiple input channels. We apply tensor decomposition models, such as CANDECOMP/PARAFAC (CP) and tensor train decomposition, in a novel way to high-dimensional and sparse data tensors, in addition to showing how they can be used for machine learning tasks, such as regression and classification. Furthermore, we illustrate how the tensor models can be extended to continuous inputs by learning a mapping from the continuous inputs to the latent representations. We apply our approach to the modeling of inverse dynamics, which is crucial for accurate feedforward robot control. Our experimental results show competitive performance of the proposed functional tensor model, with significantly decreased training and inference time when compared to state-of-the-art methods. In the third part, we show how the multi-modal information from both a statistical semantic model and a visual model can be fused to improve the task of visual relationship detection. In this sense, we combine standard visual models for object detection, based on convolutional neural networks, with latent variable models based on tensor factorization for link prediction. Specifically, we propose two approaches for the fusion of semantic and sensory information. The first approach uses a probabilistic framework, whereas the second makes use of a multi-way neural network architecture. Our experimental results on the recently published Stanford Visual Relationship dataset, a challenging real-world dataset, show that the integration of a statistical semantic model using link prediction methods can significantly improve visual relationship detection.Maschinelles Lernen zielt auf die automatische Extraktion semantischer Information aus zum Teil rohen und unstrukturierten Daten. Eine entscheidende Herausforderung beim Entwurf intelligenter Systeme, besteht darin Informationen aus verschiedenen Quellen zu extrahieren und zu fusionieren. In dieser Arbeit wird diesen Herausforderungen mit Methoden des Repräsentations-Lernens begegnet, welche eine der bedeutendsten Innovationen im Maschinellen Lernen in der letzten Dekade darstellt. Repräsentations-Lernen ist die Basis für moderne Ansätze zur Verarbeitung natürlicher Sprache und Modellierung künstlicher Neuronaler Netze, insbesondere dem Deep Learning, welchem beliebte Modelle wie Convolutional Neural Networks (CNN) und rekurrente neuronale Netze (RNN) zugeordnet werden. Außerdem wurde gezeigt, dass auch viele Ansätze zur Tensor Faktorisierung und Multi-way Modelle als Repräsentations-Lernen interpretiert werden können. Tensor Faktorisierungs Modelle finden Anwendung in einer Vielzahl von Bereichen, wie zum Beispiel der Modellierung von Wissensgraphen und der Elektroenzephalografie (EEG) Daten Analyse. Die hier vorliegende Arbeit konzentriert sich auf aktuelle Techniken des Repräsentations-Lernens, welche Information aus unterschiedlichen Kanälen kombinieren und dabei die inhärente Mehr-Kanal Struktur der Daten ausnutzen. Die Arbeit ist in drei Hauptteile gegliedert. Im ersten Teil wird die Architektur eines neuronalen Netzes beschrieben, welches zur Fusion mehrerer Repräsentationen aus unterschiedlichen Kanälen verwendet wird. Des Weiteren wird ein Attention Mechanismus vorgestellt, welcher dynamisch die gelernten Repräsentationen aus unterschiedlichen Kanälen in Abhängigkeit des aktuellen Systemzustands gewichtet. Die Methode wird zur Modellierung verteilter Sensor Netzwerke angewendet. Dabei wird die Effektivität des Ansatzes anhand dreier Datensätze mit echten Sensor Werten evaluiert. Im zweiten Teil dieser Arbeit wird untersucht, wie Tensor-Faktorisierungs Modelle zur Modellierung von Beziehungen zwischen verschiedenen Eingangs Kanälen verwendet werden können. Dabei werden Tensor Modelle wie CANDECOMP/PARAFAC (CP) und Tensor Train in einer neuartigen Art und Weise auf hochdimensionale und dünnbesetzte Tensoren angewendet. Es wird gezeigt, wie diese Modelle für Aufgaben des maschinellen Lernens, wie Regression und Klassifikation eingesetzt werden können. Desweitern wird gezeigt, wie die Tensor Modelle zu kontinuierlichen Eingangsvariablen erweitert werden können, indem eine Funktion von den kontinuierlichen Eingängen zu der latenten Repräsentation des Faktorisierungs Modells gelernt wird. Der gezeigte Ansatz wird schließlich zur Modellierung inverser Dynamiken angewandt. Die Modellierung inverser Dynamiken ist essenziell für die Vorwärtssteuerung eines Roboters. Die Experimente zeigen, dass das kontinuierliche Tensor Modell vergleichbare Ergebnisse erzielt wie herkömmliche Methoden für diese Aufgabe, wobei sich durch das Tensor Modell sowohl die Trainings als auch die Inferenz Zeit deutlich reduzieren lassen. Im dritten Teil wird gezeigt, wie die multi-modale Information eines statistisch semantischen Modells und eines visuellen Modells fusioniert werden können, um im Bereich der visuellen Infromationsextraktion, speziell dem Erkennen von Beziehungen zwischen visuellen Objekten, verbesserte Ergebnisse zu erzielen. Dabei wird ein gängiges, auf CNNs basierendes, visuelles Modell zur Objekterkennung mit Tensor-Faktorisierungs Modellen zur Modellierung von Wissensgraphen kombiniert. Es werden zwei Ansätze für die Fusion semantischer und sensorischer Information gezeigt. Der erste Ansatz benutzt eine probabilistische Methode, wohingegen der zweite Ansatz ein Multi-way neuronales Netzwerk verwendet um die Informationen zu kombinieren. Die Evaluation auf einem kürzlich veröffentlichten Datensatz (Stanford Visual Relationship Dataset), mit Bildern aus der realen Welt, zeigt, dass die Integration eines statistisch semantischen Modells, die Methoden zur Detektion visueller Objektbeziehungen deutlich verbessert

Learning from Structured Data with High Dimensional Structured Input and Output Domain

Structured data is accumulated rapidly in many applications, e.g. Bioinformatics, Cheminformatics, social network analysis, natural language processing and text mining. Designing and analyzing algorithms for handling these large collections of structured data has received significant interests in data mining and machine learning communities, both in the input and output domain. However, it is nontrivial to adopt traditional machine learning algorithms, e.g. SVM, linear regression to structured data. For one thing, the structural information in the input domain and output domain is ignored if applying the normal algorithms to structured data. For another, the major challenge in learning from many high-dimensional structured data is that input/output domain can contain tens of thousands even larger number of features and labels. With the high dimensional structured input space and/or structured output space, learning a low dimensional and consistent structured predictive function is important for both robustness and interpretability of the model. In this dissertation, we will present a few machine learning models that learn from the data with structured input features and structured output tasks. For learning from the data with structured input features, I have developed structured sparse boosting for graph classification, structured joint sparse PCA for anomaly detection and localization. Besides learning from structured input, I also investigated the interplay between structured input and output under the context of multi-task learning. In particular, I designed a multi-task learning algorithms that performs structured feature selection & task relationship Inference. We will demonstrate the applications of these structured models on subgraph based graph classification, networked data stream anomaly detection/localization, multiple cancer type prediction, neuron activity prediction and social behavior prediction. Finally, through my intern work at IBM T.J. Watson Research, I will demonstrate how to leverage structural information from mobile data (e.g. call detail record and GPS data) to derive important places from people's daily life for transit optimization and urban planning

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters