7 citations · 8 across the 8 of their papers we have counts for
8 papers
In-batch Relational Features Enhance Precision in An Unsupervised Medical Anomaly Detection Task
P. Bilha Githinji, Ijaz Gul, Lian Zhang +3
Confounding pathology with normal anatomical variation remains a significant challenge in unsupervised medical-image anomaly detection, resulting in numerous false positives. To en…
pLDDT-Predictor: High-speed Protein Screening Using Transformer and ESM2
Joongwon Chae, Zhenyu Wang, Ijaz Gul +3
Recent advancements in protein structure prediction, particularly AlphaFold2, have revolutionized structural biology by achieving near-experimental accuracy ($\text{average RMSD} <…
COVID-19: post infection implications in different age groups, mechanism, diagnosis, effective prevention, treatment, and recommendations
Muhammad Akmal Raheem, Muhammad Ajwad Rahim, Ijaz Gul +9
SARS-CoV-2, the highly contagious pathogen responsible for the COVID-19 pandemic, has persistent effects that begin four weeks after initial infection and last for an undetermined…
Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy
Boyuan Peng, Jiaju Chen, P. Bilha Githinji +9
Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have…
Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection
P. Bilha Githinji, Xi Yuan, Zhenglin Chen +8
Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these…
Current and perspective sensing methods for monkeypox virus: a reemerging zoonosis in its infancy
Ijaz Gul, Changyue Liu, Yuan Xi +12
Objectives The review is dedicated to evaluate the current monkeypox virus (MPXV) detection methods, discuss their pros and cons, and provide recommended solutions to the problems.…
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Muhammad Hassan, Haifei Guan, Aikaterini Melliou +24
Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safet…
RCMNet: A deep learning model assists CAR-T therapy for leukemia
Ruitao Zhang, Xueying Han, Ijaz Gul +8
Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although…