Telmo Amaral

Learn More
The overall purpose of the research discussed here is the enhancement of home-based care by revealing individual patterns in the life of a person, through modelling of the "busyness" of activity in their dwelling, so that care can be better tailored to their needs and changing circumstances. The use of data mining and on-line analytical processing (OLAP) is(More)
BACKGROUND Tissue microarrays (TMAs) are an important tool in translational research for examining multiple cancers for molecular and protein markers. Automatic immunohistochemical (IHC) scoring of breast TMA images remains a challenging problem. METHODS A two-stage approach that involves localization of regions of invasive and in-situ carcinoma followed(More)
—Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since auto-encoders aim to(More)
—Deep architectures have been used in transfer learning applications, with the aim of improving the performance of networks designed for a given problem by reusing knowledge from another problem. In this work we addressed the transfer of knowledge between deep networks used as classifiers of digit and shape images, considering cases where only the set of(More)
Breast tissue microarrays facilitate the survey of very large numbers of tumours but their scoring by pathologists is time consuming, typically highly quantised and not without error. Automated seg-mentation of cells and intra-cellular compartments in such data can be problematic for reasons that include cell overlapping, complex tissue structure, debris,(More)
Tissue microarrays (TMAs) facilitate the survey of very large numbers of tumors. However, the manual assessment of stained TMA sections constitutes a bottleneck in the pathologist's work flow. This paper presents a computational pipeline for automatically classifying and scoring breast cancer TMA spots that have been subjected to nuclear immunostaining.(More)
We present Speeching, a mobile application that uses crowdsourcing to support the self-monitoring and management of speech and voice issues for people with Parkinson's (PwP). The application allows participants to audio record short voice tasks, which are then rated and assessed by crowd workers. Speeching then feeds these results back to provide users with(More)