Christian Gagné

Revisiting Data Augmentation for Ultrasound Images

Adam Tupper

Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite o… (see more)ften facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.

2025-01-22

ArXiv (preprint)

Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers

Fatemeh Nourilenjan Nokabadi

Jean-Francois Lalonde

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.

2024-11-26

ArXiv (preprint)

Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers

Fatemeh Nourilenjan Nokabadi

Jean-Francois Lalonde

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.

2024-11-26

ArXiv (preprint)

Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers

Fatemeh Nourilenjan Nokabadi

Jean-Francois Lalonde

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.

2024-10-15

NeurIPS.cc/2024/Workshop/AdvML-Frontiers (published)

TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers

Fatemeh Nourilenjan Nokabadi

Yann Batiste Pequignot

Jean-Francois Lalonde

Adversarial perturbations can deceive neural networks by adding small, imperceptible noise to the input. Recent object trackers with transfo… (see more)rmer backbones have shown strong performance on tracking datasets, but their adversarial robustness has not been thoroughly evaluated. While transformer trackers are resilient to black-box attacks, existing white-box adversarial attacks are not universally applicable against these new transformer trackers due to differences in backbone architecture. In this work, we introduce TrackPGD, a novel white-box attack that utilizes predicted object binary masks to target robust transformer trackers. Built upon the powerful segmentation attack SegPGD, our proposed TrackPGD effectively influences the decisions of transformer-based trackers. Our method addresses two primary challenges in adapting a segmentation attack for trackers: limited class numbers and extreme pixel class imbalance. TrackPGD uses the same number of iterations as other attack methods for tracker networks and produces competitive adversarial examples that mislead transformer and non-transformer trackers such as MixFormerM, OSTrackSTS, TransT-SEG, and RTS on datasets including VOT2022STS, DAVIS2016, UAV123, and GOT-10k.

2024-10-15

NeurIPS.cc/2024/Workshop/AdvML-Frontiers (published)

TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers

Fatemeh Nourilenjan Nokabadi

Yann Batiste Pequignot

Jean-Francois Lalonde

2024-10-15

NeurIPS.cc/2024/Workshop/AdvML-Frontiers (published)

A Layer Selection Approach to Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Batiste Pequignot

Frederic Precioso

Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (see more) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.

2024-10-10

NeurIPS.cc/2024/Workshop/FITML (poster)

A Layer Selection Approach to Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Batiste Pequignot

Frederic Precioso

Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (see more) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.

2024-10-10

NeurIPS.cc/2024/Workshop/FITML (poster)

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

Jonas Ngnawe

Sabyasachi Sahoo

Yann Batiste Pequignot

Frederic Precioso

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning

Frédéric Beaupré

Anthony Bilodeau

Theresa Wiesner

Gabriel Leclerc

Mado Lemieux

Gabriel Nadeau

Katrine Castonguay

Bolin Fan

Simon Labrecque

Renée Hložek

Paul De Koninck

Flavie Lavoie-Cardinal

Ca2+ imaging methods are widely used for studying cellular activity in the brain, allowing detailed analysis of dynamic processes across var… (see more)ious scales. Enhanced by high-contrast optical microscopy and fluorescent Ca2+ sensors, this technique can be used to reveal localized Ca2+ fluctuations within neurons, including in sub-cellular compartments, such as the dendritic shaft or spines. Despite advances in Ca2+ sensors, the analysis of miniature Synaptic Calcium Transients (mSCTs), characterized by variability in morphology and low signal-to-noise ratios, remains challenging. Traditional threshold-based methods struggle with the detection and segmentation of these small, dynamic events. Deep learning (DL) approaches offer promising solutions but are limited by the need for large annotated datasets. Positive Unlabeled (PU) learning addresses this limitation by leveraging unlabeled instances to increase dataset size and enhance performance. This approach is particularly useful in the case of mSCTs that are scarce and small, associated with a very small proportion of the foreground pixels. PU learning significantly increases the effective size of the training dataset, improving model performance. Here, we present a PU learning-based strategy for detecting and segmenting mSCTs. We evaluate the performance of two 3D deep learning models, StarDist-3D and 3D U-Net, which are well established for the segmentation of small volumetric structures in microscopy datasets. By integrating PU learning, we enhance the 3D U-Net’s performance, demonstrating significant gains over traditional methods. This work pioneers the application of PU learning in Ca2+ imaging analysis, offering a robust framework for mSCT detection and segmentation. We also demonstrate how this quantitative analysis pipeline can be used for subsequent mSCTs feature analysis. We characterize morphological and kinetic changes of mSCTs associated with the application of chemical long-term potentiation (cLTP) stimulation in cultured rat hippocampal neurons. Our data-driven approach shows that a cLTP-inducing stimulus leads to the emergence of new active dendritic regions and differently affects mSCTs subtypes.

2024-07-08

bioRxiv (preprint)

TrackPGD: A White-box Attack using Binary Masks against Robust Transformer Trackers

Fatemeh Nourilenjan Nokabadi

Yann Batiste Pequignot

Jean-Francois Lalonde

2024-07-04

ArXiv (preprint)