Search Results for author: Thomas Fevens

Found 10 papers, 3 papers with code

Surface Realization Using Pretrained Language Models

no code implementations MSR (COLING) 2020 Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens

In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar.

Language Modelling Text Generation

on the effectiveness of generative adversarial network on anomaly detection

1 code implementation31 Dec 2021 Laya Rafiee Sevyeri, Thomas Fevens

Identifying anomalies refers to detecting samples that do not resemble the training data distribution.

Anomaly Detection

Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation

no code implementations26 Jan 2021 Prabhakara Subramanya Jois, Aniketh Manjunath, Thomas Fevens

With the emergence and advancements of deep learning for digital healthcare, several methodologies have been proposed for such segmentation tasks.

Style Transfer

FoCL: Feature-Oriented Continual Learning for Generative Models

1 code implementation9 Mar 2020 Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei, Francis Dutil, Thomas Fevens, Mohammad Havaei

In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).

Continual Learning Incremental Learning

The Concordia NLG Surface Realizer at SRST 2019

no code implementations WS 2019 Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens

This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task.

The TCGA Meta-Dataset Clinical Benchmark

1 code implementation18 Oct 2019 Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sebastien Lemieux, Joseph Paul Cohen

Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.

Decision Making

Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks

no code implementations28 May 2019 Qicheng Lao, Thomas Fevens

In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined.

General Classification

Leveraging Disease Progression Learning for Medical Image Recognition

no code implementations26 Jun 2018 Qicheng Lao, Thomas Fevens, Boyu Wang

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning.

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