Author details
Author's Published Works
Author details
Showing 1-25 of 95 results
- Sort
Filter Results
Show
Sort By
Results
Continual-Zoo: Leveraging Zoo Models for Continual Classification of Medical Images
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
In medical imaging, leveraging continual learning (CL) is key for models to adapt to new classes and data distributions without forgetting prior knowledge. Existing CL methods often overlook the use of off-the-shelf pretrained models that are equipped with informative and generalizable representations, opting instead to learn from scratch. In this paper, we propose Continual-Zoo, a novel CL paradi...Show More
Deep learning models have achieved remarkable success in medical image classification. These models are typically trained once on the available annotated images and thus lack the ability of continually learning new tasks (i.e., new classes or data distributions) due to the problem of catastrophic forgetting. Recently, there has been more interest in designing continual learning methods to learn di...Show More
Learning-to-Count by Learning-to-Rank
2023 20th Conference on Robots and Vision (CRV)
Year: 2023 | Conference Paper |
Object counting methods rely on density maps, which are heatmaps produced by placing Gaussian density over object locations. However, density maps are expensive to collect. To reduce the annotation burden, we propose a form of weak supervision that only requires object-based pairwise image rankings. These annotations can be collected rapidly with a single click per image pair and supply a weak sig...Show More
BoosterNet: Improving Domain Generalization of Deep Neural Nets using Culpability-Ranked Features
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Deep learning (DL) models trained to minimize empirical risk on a single domain often fail to generalize when applied to other domains. Model failures due to poor generalizability are quite common in practice and may prove quite perilous in mission-critical applications, e.g., diagnostic imaging where real-world data often exhibits pronounced variability. Such limitations have led to increased int...Show More
DEEMD: Drug Efficacy Estimation Against SARS-CoV-2 Based on Cell Morphology With Deep Multiple Instance Learning
M. Sadegh Saberian;Kathleen P. Moriarty;Andrea D. Olmstead;Christian Hallgrimson;François Jean;Ivan R. Nabi;Maxwell W. Libbrecht;Ghassan Hamarneh
Drug repurposing can accelerate the identification of effective compounds for clinical use against SARS-CoV-2, with the advantage of pre-existing clinical safety data and an established supply chain. RNA viruses such as SARS-CoV-2 manipulate cellular pathways and induce reorganization of subcellular structures to support their life cycle. These morphological changes can be quantified using bioimag...Show More
Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography
Hanene Ben Yedder;Ben Cardoen;Majid Shokoufi;Farid Golnaraghi;Ghassan Hamarneh
Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconst...Show More
D-LEMA: Deep Learning Ensembles from Multiple Annotations - Application to Skin Lesion Segmentation
Zahra Mirikharaji;Kumar Abhishek;Saeed Izadi;Ghassan Hamarneh
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (16)
Medical image segmentation annotations suffer from inter- and intra-observer variations even among experts due to intrinsic differences in human annotators and ambiguous boundaries. Leveraging a collection of annotators’ opinions for an image is an interesting way of estimating a gold standard. Although training deep models in a supervised setting with a single annotation per image has been extens...Show More
Matthews Correlation Coefficient Loss For Deep Convolutional Networks: Application To Skin Lesion Segmentation
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (17)
The segmentation of skin lesions is a crucial task in clinical decision support systems for the computer aided diagnosis of skin lesions. Although deep learning-based approaches have improved segmentation performance, these models are often susceptible to class imbalance in the data, particularly, the fraction of the image occupied by the background healthy skin. Despite variations of the popular ...Show More
Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation
Kidney volume is an essential biomarker for a number of kidney disease diagnoses, for example, chronic kidney disease. Existing total kidney volume estimation methods often rely on an intermediate kidney segmentation step. On the other hand, automatic kidney localization in volumetric medical images is a critical step that often precedes subsequent data processing and analysis. Most current approa...Show More
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images. Although deep learning-based approaches have considerably improved the segmentation accuracy, there is still room for improvement by addressing the major challenges, such as variations in lesion shape, size, color and varying levels of contrast. In this work, we ...Show More
Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
Shahab Aslani;Vittorio Murino;Michael Dayan;Roger Tam;Diego Sona;Ghassan Hamarneh
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Year: 2020 | Conference Paper |
Cited by: Papers (29)
This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts. We propose to integrate a traditional encoder-decoder network with a regularization network. This added network includes an auxiliary loss term which is responsible for th...Show More
ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy
Ben Cardoen;Hanene Ben Yedder;Anmol Sharma;Keng C Chou;Ivan Robert Nabi;Ghassan Hamarneh
Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fl...Show More
Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network
Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in the form of MR pulse sequences in a single scan provides valuable insights to physicians, as well as enabling automated systems performing downstream analysis. However, many issues like prohibitive scan time, image...Show More
Retinal Image Classification via Vasculature-Guided Sequential Attention
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
Age-related macular degeneration and diabetic retinopathy are diseases of increasing prevalence globally in recent years. Traditionally, diagnosing these diseases relied on manual visual inspection by experts, which was costly, time-consuming and laborious as it required closely examining high-resolution color fundus images. More recently, deep learning networks have shown great potential in predi...Show More
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (8)
The accuracy of medical imaging-based diagnostics is directly impacted by the quality of the collected images. A passive approach to improve image quality is one that lags behind improvements in imaging hardware, awaiting better sensor technology of acquisition devices. An alternative, active strategy is to utilize prior knowledge of the imaging system to directly post-process and improve the acqu...Show More
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
Saeid Asgari Taghanaki;Kumar Abhishek;Shekoofeh Azizi;Ghassan Hamarneh
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (20)
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linear radial basis convolutional feature mapping by learning a Mahalanobis-like distance function. Our method then maps the convolutional features onto a linearly well-separated manifold, which prevents small adversarial per...Show More
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features
The presence of certain clinical dermoscopic features within a skin lesion may indicate melanoma, and automatically detecting these features may lead to more quantitative and reproducible diagnoses. We reformulate the task of classifying clinical dermoscopic features within superpixels as a segmentation problem, and propose a fully convolutional neural network to detect clinical dermoscopic featur...Show More
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets
Jeremy Kawahara;Sara Daneshvar;Giuseppe Argenziano;Ghassan Hamarneh
We propose a multitask deep convolutional neural network, trained on multimodal data (clinical and dermoscopic images, and patient metadata), to classify the 7-point melanoma checklist criteria and perform skin lesion diagnosis. Our neural network is trained using several multitask loss functions, where each loss considers different combinations of the input modalities, which allows our model to b...Show More
Joint Gender-, Tone-, Vowel- Classification Via Novel Hierarchical Classification for Annotation of Monosyllabic Mandarin Word Tokens
Saurabh Garg;Ghassan Hamarneh;Allard Jongman;Joan Sereno;Yue Wang
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
The automatic annotation of Mandarin monosyllabic audio word tokens remains an important yet challenging issue in phonetics research. In this work, we address this annotation task via a novel subcategories-classification framework that not only performs word identification via the joint classifications of vowel and tone subcategories, but also performs gender discrimination of the speaker, which s...Show More
Connectome priors in deep neural networks to predict autism
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Year: 2018 | Conference Paper |
Cited by: Papers (26)
We propose a novel element-wise layer for deep neural networks that incorporates general priors designed for connectomes. In contrast to regular images, connectomes, expressed as adjacency matrices, are composed of elements that capture a relationship between two brain regions. As each element in the connectome has an anatomical meaning that is consistent across samples, prior knowledge about the ...Show More
Generative adversarial networks to segment skin lesions
Saeed Izadi;Zahra Mirikharaji;Jeremy Kawahara;Ghassan Hamarneh
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Year: 2018 | Conference Paper |
The accuracy of skin lesion segmentation has increased in recent years, thanks to advances in machine learning techniques and a large influx of dermoscopy images. However, there is still room for improvement as there exist many considerable challenges mainly due to the large variability in the appearance of lesions (i.e., shape, size, texture, and occlusions). In this work, we present a novel appr...Show More
Deep auto-context fully convolutional neural network for skin lesion segmentation
Zahra Mirikharaji;Saeed Izadi;Jeremy Kawahara;Ghassan Hamarneh
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Year: 2018 | Conference Paper |
Cited by: Papers (11)
Automatic segmentation of skin lesions in dermoscopy and clinical images is a common initial step in computer aided diagnosis. However, the low contrast of lesion boundaries and the existence of misleading image artifacts (e.g., hair), make segmenting skin lesions a challenging task. We propose a deep auto-context architecture that incorporates image appearance information as well as contextual in...Show More
It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diagnosis. Similarly, automatic histopathology image analysis algorithms rely on color or intensity measures to extract tissue features. With the increasi...Show More
Segmentation and Measurement of Chronic Wounds for Bioprinting
Peyman Gholami;Mohammad Ali Ahmadi-pajouh;Nabiollah Abolftahi;Ghassan Hamarneh;Mohammad Kayvanrad
Objective: to provide a proof-of-concept tool for segmenting chronic wounds and transmitting the results as instructions and coordinates to a bioprinter robot and thus facilitate the treatment of chronic wounds. Methods: several segmentation methods used for measuring wound geometry, including edge-detection and morphological operations, region-growing, Livewire, active contours, and texture segme...Show More
Leveraging Tree Statistics for Extracting Anatomical Trees from 3D Medical Images
2017 14th Conference on Computer and Robot Vision (CRV)
Year: 2017 | Conference Paper |
Cited by: Papers (3)
Using different priors (e.g. shape and appearance) have proven critical for robust image segmentation of different types of target objects. Many existing methods for extracting trees (e.g. vascular or airway trees) from medical images have leveraged appearance priors (e.g. tubular-ness and bifurcationness) and the knowledge of the cross-sectional geometry (e.g. circles or ellipses) of the tree-for...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.