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A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics.
BMC Bioinformatics
January 2025
Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, 14115-111, Iran.
Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets.
Proc SPIE Int Soc Opt Eng
February 2024
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.
Volumetric assessment of edema due to anasarca can help monitor the progression of diseases such as kidney, liver or heart failure. The ability to measure edema non-invasively by automatic segmentation from abdominal CT scans may be of clinical importance. The current state-of-the-art method for edema segmentation using intensity priors is susceptible to false positives or under-segmentation errors.
View Article and Find Full Text PDFEndoSRR: a comprehensive multi-stage approach for endoscopic specular reflection removal.
Int J Comput Assist Radiol Surg
June 2024
Faculty of Data Science, City University of Macau, Macau, China.
Purpose: Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal.
Methods: We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images.
Effects of sceptical priors on the performance of adaptive clinical trials with binary outcomes.
Pharm Stat
November 2024
Department of Intensive Care 4131, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi-stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%.
View Article and Find Full Text PDFWeakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training.
Int ACM SIGIR Conf Res Dev Inf Retr
July 2023
Emory University, Atlanta, GA, USA.
Scientific document classification is a critical task for a wide range of applications, but the cost of collecting human-labeled data can be prohibitive. We study scientific document classification using label names only. In scientific domains, label names often include domain-specific concepts that may not appear in the document corpus, making it difficult to match labels and documents precisely.
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