65 citations · 115 across the 23 of their papers we have counts for
23 papers
RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Text-guided zero-shot object counters excel at spatial localization but categorize poorly on novel or fine-grained classes: natural language is too coarse to fully specify visual i…
Spectral Effects Of Heavy-Tailed Vertex Noise In Geometric Graphs
Ben Cardoen, Jeremy Budd, Enrico Amico +2
We characterize which local matrix structures saturate Weyl's eigenvalue perturbation bound for graph Laplacians under geometrically constrained vertex displacements. Geometric gra…
Making Power Explicable in AI: Analyzing, Understanding, and Redirecting Power to Operationalize Ethics in AI Technical Practice
Weina Jin, Elise Li Zheng, Ghassan Hamarneh
The operationalization of ethics in the technical practices of artificial intelligence (AI) is facing significant challenges. To address the problem of ineffective implementation o…
What Can We Learn from Inter-Annotator Variability in Skin Lesion Segmentation?
Kumar Abhishek, Jeremy Kawahara, Ghassan Hamarneh
Medical image segmentation exhibits intra- and inter-annotator variability due to ambiguous object boundaries, annotator preferences, expertise, and tools, among other factors. Les…
Ethical Medical Image Synthesis
Weina Jin, Ashish Sinha, Kumar Abhishek +1
The task of ethical Medical Image Synthesis (MISyn) is to ensure that the MISyn techniques are researched and developed ethically throughout their entire lifecycle, which is essent…
FiGO: Fine-Grained Object Counting without Annotations
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Class-agnostic counting (CAC) methods reduce annotation costs by letting users define what to count at test-time through text or visual exemplars. However, current open-vocabulary…
AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"
Weina Jin, Nicholas Vincent, Ghassan Hamarneh
"why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore th…
Disentangled PET Lesion Segmentation
Tanya Gatsak, Kumar Abhishek, Hanene Ben Yedder +2
PET imaging is an invaluable tool in clinical settings as it captures the functional activity of both healthy anatomy and cancerous lesions. Developing automatic lesion segmentatio…
SMITE: Segment Me In TimE
Amirhossein Alimohammadi, Sauradip Nag, Saeid Asgari Taghanaki +3
Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty incre…
Lesion Elevation Prediction from Skin Images Improves Diagnosis
Kumar Abhishek, Ghassan Hamarneh
While deep learning-based computer-aided diagnosis for skin lesion image analysis is approaching dermatologists' performance levels, there are several works showing that incorporat…
Segmentation Style Discovery: Application to Skin Lesion Images
Kumar Abhishek, Jeremy Kawahara, Ghassan Hamarneh
Variability in medical image segmentation, arising from annotator preferences, expertise, and their choice of tools, has been well documented. While the majority of multi-annotator…
AFreeCA: Annotation-Free Counting for All
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Object counting methods typically rely on manually annotated datasets. The cost of creating such datasets has restricted the versatility of these networks to count objects from spe…
Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
Kumar Abhishek, Aditi Jain, Ghassan Hamarneh
The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large…
SYRAC: Synthesize, Rank, and Count
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Crowd counting is a critical task in computer vision, with several important applications. However, existing counting methods rely on labor-intensive density map annotations, neces…
SLiMe: Segment Like Me
Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi +2
Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence,…
AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
Ivan R. Nabi, Ben Cardoen, Ismail M. Khater +3
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell,…
Why is plausibility surprisingly problematic as an XAI criterion?
Weina Jin, Xiaoxiao Li, Ghassan Hamarneh
Explainable artificial intelligence (XAI) is motivated by the problem of making AI predictions understandable, transparent, and responsible, as AI becomes increasingly impactful in…
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals
Weina Jin, Jianyu Fan, Diane Gromala +2
Non-technical end-users are silent and invisible users of the state-of-the-art explainable artificial intelligence (XAI) technologies. Their demands and requirements for AI explain…
Log-Paradox: Necessary and sufficient conditions for confounding statistically significant pattern reversal under the log-transform
Ben Cardoen, Hanene Ben Yedder, Sieun Lee +2
The log-transform is a common tool in statistical analysis, reducing the impact of extreme values, compressing the range of reported values for improved visualization, enabling the…
Transcending XAI Algorithm Boundaries through End-User-Inspired Design
Weina Jin, Jianyu Fan, Diane Gromala +3
The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability. This research parad…
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
Weina Jin, Xiaoxiao Li, Ghassan Hamarneh
Being able to explain the prediction to clinical end-users is a necessity to leverage the power of artificial intelligence (AI) models for clinical decision support. For medical im…
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 +5
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 establ…
EUCA: the End-User-Centered Explainable AI Framework
Weina Jin, Jianyu Fan, Diane Gromala +2
The ability to explain decisions to end-users is a necessity to deploy AI as critical decision support. Yet making AI explainable to non-technical end-users is a relatively ignored…