3 citations · 7 across the 6 of their papers we have counts for
6 papers
Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation
Zhihua Liu, Amrutha Saseendran, Lei Tong +8
Open-set image segmentation poses a significant challenge because existing methods often demand extensive training or fine-tuning and generally struggle to segment unified objects…
MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans
Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre +13
Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subje…
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations
Piotr Kaniewski, Fariba Yousefi, Yeman Brhane Hagos +2
In drug discovery, accurate lung tumor segmentation is an important step for assessing tumor size and its progression using \textit{in-vivo} imaging such as MRI. While deep learnin…
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
Che Liu, Zhongwei Wan, Haozhe Wang +6
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically r…
Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation
Irina Zhang, Jim Denholm, Azam Hamidinekoo +10
Accurate segmentation of glomerulus instances attains high clinical significance in the automated analysis of renal biopsies to aid in diagnosing and monitoring kidney disease. Ana…
Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning
Ricardo Mokhtari, Azam Hamidinekoo, Daniel Sutton +12
Crohn's Disease (CD) and Ulcerative Colitis (UC) are the two main Inflammatory Bowel Disease (IBD) types. We developed deep learning models to identify histological disease feature…