Portugal

Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! In this talk I will show how machine learning is transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in time-series, causal inference, interpretable and explainable machine learning, as well as the development of new machine learning areas - quantitative epistemology.

ICPRAM 2022

Machine Learning for Medicine and Healthcare

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.<br><br>CONFERENCE AREAS<br>
1 . Theory and Methods<br>
2 . Applications

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.

ENDO 2021

ENDO is the leading meeting for endocrinology research and clinical care worldwide. 

keynote

Matching is a process of finding correspondences (transforming, registering) between different sets of data (multiple images or videos, data from different sensors, times, depths, or viewpoints). It is used in robotics, computer vision, surveillance, medical imaging, remote sensing etc. I will present our recent works on image matching and localisation, how we were inspired by ideas proposed in the past, and how we implemented these ideas with the modern tools to advance the field. In past, significant attention was paid to the efficiency of new approaches for feature extraction, but this has been less targeted in deep learning techniques. I will discuss how the challenges have changed and how we address this issue. I will talk about the remaining problems in local feature extraction and their continuing relevance in computer vision applications, in particular large scale camera localisation, where classical methods and local features still remain competitive.

Everything Has Been Done, It Is Our Job to Do It One Better in Image Matching and Localisation

Video generation consists of generating a video sequence so that an object in a source image is animated according to some external information (a conditioning label or the motion of a driving video). In this talk I will present some of our recent achievements adressing these specific aspects: 1) generating facial expressions, e.g., smiles that are different from each other (e.g., spontaneous, tense, etc.) using diversity as the driving force. 2) generating videos without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g. faces, human bodies), our method can be applied to any object of this class. To achieve this, we decouple appearance and motion information using a self-supervised formulation. To support complex motions, we use a representation consisting of a set of learned keypoints along with their local affine transformations. A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video. Our solutions score best on diverse benchmarks and on a variety of object categories.

Image and Video Generation: A Deep Learning Approach

Traditional machine learning assumes training and test data are sampled from the same distribution in an i.i.d. fashion. However, in practice, this assumption is often violated. Therefore, in continual learning, alternative settings are studied, where systems are to be updated with new knowledge over time, with minimal forgetting of what they have learnt in the past. In this talk, I'll give an overview of some of our contributions to this field. In particular, I'll make a case for the use of prototypical networks and the need for a more in-depth analysis of a network's behaviour when learning continually. I'll finish with some open challenges and new continual learning settings beyond the standard task/class/domain incremental learning.

Machine Learning for Medicine and Healthcare

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Everything Has Been Done, It Is Our Job to Do It One Better in Image Matching and Localisation

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