65 citations · 115 across the 23 of their papers we have counts for
6 papers · 1 filter
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…