1 citations · 1 across the 2 of their papers we have counts for
6 papers
Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
Kumar Abhishek, Ghassan Hamarneh
Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) a…
IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset
Kumar Abhishek, Jeremy Kawahara, Ghassan Hamarneh
Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermoscopic skin lesion imaging allows h…
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…
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…
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…