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Found 15 papers, 7 papers with code
Application of Homomorphic Encryption in Medical Imaging
In this technical report, we explore the use of homomorphic encryption (HE) in the context of training and predicting with deep learning (DL) models to deliver strict \textit{Privacy by Design} services, and to enforce a zero-trust model of data governance.
Cross-Modal Information Maximization for Medical Imaging: CMIM
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.)
FoCL: Feature-Oriented Continual Learning for Generative Models
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).
The TCGA Meta-Dataset Clinical Benchmark
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.
Saliency is a Possible Red Herring When Diagnosing Poor Generalization
In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction.
Underwhelming Generalization Improvements From Controlling Feature Attribution
We describe a simple method for taking advantage of such auxiliary labels, by training networks to ignore the distracting features which may be extracted outside of the region of interest, on the training images for which such masks are available.
Dual Adversarial Inference for Text-to-Image Synthesis
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
GradMask: Reduce Overfitting by Regularizing Saliency
With too few samples or too many model parameters, overfitting can inhibit the ability to generalise predictions to new data.
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures.
Towards the Latent Transcriptome
In this work we propose a method to compute continuous embeddings for kmers from raw RNA-seq data, without the need for alignment to a reference genome.
Towards Gene Expression Convolutions using Gene Interaction Graphs
We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used.
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
We investigate the integration of a planning mechanism into sequence-to-sequence models using attention.
Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning
We investigate the integration of a planning mechanism into an encoder-decoder architecture with attention.
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation.
Adversarial Generation of Natural Language
Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation.
