3.3k citations · 3.9k across the 24 of their papers we have counts for
11 papers · 1 filter
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei +1
Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance on a single modality, neural networks can…
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi +4
Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is…
DFNet: Discriminative feature extraction and integration network for salient object detection
Mehrdad Noori, Sina Mohammadi, Sina Ghofrani Majelan +2
Despite the powerful feature extraction capability of Convolutional Neural Networks, there are still some challenges in saliency detection. In this paper, we focus on two aspects o…
FoCL: Feature-Oriented Continual Learning for Generative Models
Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei +3
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL). Unlike previous works that aim to solve the c…
CAGNet: Content-Aware Guidance for Salient Object Detection
Sina Mohammadi, Mehrdad Noori, Ali Bahri +2
Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results. However, it is still challenging to learn effective features…
Dual Adversarial Inference for Text-to-Image Synthesis
Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader +3
Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color,…
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier +4
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic dis…
HeMIS: Hetero-Modal Image Segmentation
Mohammad Havaei, Nicolas Guizard, Nicolas Chapados +1
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the pr…
Deep learning trends for focal brain pathology segmentation in MRI
Mohammad Havaei, Nicolas Guizard, Hugo Larochelle +1
Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, su…
Within-Brain Classification for Brain Tumor Segmentation
Mohammad Havaei, Hugo Larochelle, Philippe Poulin +1
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a mach…
Brain Tumor Segmentation with Deep Neural Networks
Mohammad Havaei, Axel Davy, David Warde-Farley +6
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and…