In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
Sensory input from multiple sources is crucial for robust and coherent human perception.
Zero-shot classification is a generalization task where no instance from the target classes is seen during training.
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as the different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.)
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.
In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well.
Ranked #1 on Layout-to-Image Generation on COCO-Stuff 256x256
In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL).
In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce.
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.
5 code implementations • 28 Nov 2016 • Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, Yoshua Bengio
It is based on the idea that we can first learn or provide a distributed representation for each input feature (e. g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units).