Event
3 min read
•March 1, 2026Invited Talk: ROBOTICS-2026 (Rome) — Multimodal AI and Real-World Deployment
By CredenceX Research Team
Our team will present ongoing research on multimodal learning, explainability, and deployment-ready AI systems.
Welcome to CredenceX AI Research Lab
Building Intelligent Systems That Sense, Reason, and Assist
CredenceX AI Research Lab is an independent research initiative advancing trustworthy, explainable, and socially beneficial artificial intelligence. We develop data-driven solutions for medical imaging to support early warning, clinical decision support, and risk-aware deployment in high-stakes environments. We aim to reduce disparities by ensuring our models perform reliably across devices and diverse patient populations. We also provide clear, clinically meaningful explanations and uncertainty, helping clinicians and patients know when to trust an output and when to be cautious.
Core Research Areas at CredenceX
Exploring cutting-edge technologies to build safer, smarter, and more trustworthy AI systems for tomorrow
01
Vision + Language for Healthcare
Link images, reports, and clinical context to support multimodal understanding and decision support.
02
Explainable Imaging AI
Design explanations and evidence signals that align with clinical interpretation and workflow constraints.
03
Trustworthy & Risk-Aware AI
Advance reliability testing, uncertainty-aware outputs, calibration, and auditable decision support.
04
Efficient AI at the Edge
Build lightweight models for real-time use on web, mobile, and resource-limited devices.
05
Human-in-the-Loop Decision Support
Develop decision pipelines that preserve clinician control and communicate risk transparently.
06
Robust Across Sites & Scanners
Focus on cross-hospital generalization to reduce performance degradation in real-world settings.
Latest News from CredenceX
Stay updated with our recent achievements, announcements, and research breakthroughs
Event
3 min read
•March 1, 2026Invited Talk: BEACONGRESS2026 (Portugal) — Trustworthy Clinical AI
By CredenceX Research Team
Sharing progress on explainable decision support and calibration-aware medical AI workflows.
Announcement
2 min read
•March 1, 2026Conference Service: Organizer Role at IIMCSE 2026
By CredenceX Research Team
Contributing to the research community through conference organization and technical coordination.
Award
3 min read
•December 5, 2025Best Paper Award: IEEE PEEIACON 2025
By CredenceX Research Team
Awarded for research excellence at the 2025 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON).
Featured Projects at CredenceX
Showcasing our cutting-edge research projects that push the boundaries of AI innovation and real-world impact
Trustworthy & Calibrated AI
completed
DepTformer-XAI-SV
2025A reproducible, explainable transformer pipeline for depression emotion/severity experiments, including ablations, XAI faithfulness checks, and a minimal Flask demo (research use only).
PyTorchPythonFlaskDocker
Explainable Medical Image Intelligence
completed
Explainable Lung Cancer Diagnosis
2025Lightweight hybrid CNN–Transformer (MobileViT + attention + texture cues) for efficient and explainable lung cancer diagnosis on CT/histopathology with Grad-CAM and robust evaluation support.
PyTorchMobileViTCBAMGrad-CAM
Multimodal Vision–Language Foundation Models
completed
Multimodal Information Fusion
2025A modular pipeline for audio-visual object recognition using hybrid, tensor, and FiLM-style fusion with flexible feature extraction and noise-robust training options.
PythonFiLM FusionXceptionxLSTM
Efficient Hybrid Transformers for Edge Deployment
completed
CottonVerse
2025Flask-based web application for cotton leaf disease, fabric stain defect detection, and fabric composition classification with probability charts and Grad-CAM explanations.
FlaskPyTorchtimmpytorch-grad-cam
Research Publications at CredenceX
Cutting-edge research contributions advancing the field of artificial intelligence
Journal
Published
Vision-audio multimodal object recognition using hybrid and tensor fusion techniques
Md Redwan Ahmed, Rezaul Haque, SM Arafat Rahman, Ahmed Wasif Reza, Nazmul Siddique, Hui Wang
Information Fusion (Elsevier)
Audio–visual multimodal object recognition with hybrid + tensor fusion strategies designed for robust real-world performance.
Journal
Published
Ensemble Transformer with Post-hoc Explanations for Depression Emotion and Severity Detection
Sazzadul Islam, Rezaul Haque, Mahbub Alam Khan, Arafath Bin Mohiuddin, Md Ismail Hossain Siddiqui, Zishad Hossain Limon, Katura Gania Khushbu, SM Masfequier Rahman Swapno, Md Redwan Ahmed, Abhishek Appaji
iScience (Cell Press / Elsevier)
DepTformer-XAI-SV: ensemble transformers for depression emotion/severity detection with LIME explanations and a web app.
Journal
Published
Explainable Transformer Framework for Fast Cotton Leaf Diagnostics and Fabric Defect Detection
SM Masfequier Rahman Swapno, Anamul Sakib, Amira Hossain, Jesika Debnath, Abdullah Al Noman, Abdullah Al Sakib, Md Redwan Ahmed, Rezaul Haque, Abhishek Appaji
iScience (Cell Press / Elsevier)
Explainable transformer framework spanning agriculture (cotton leaf) and textile inspection (fabric defect) with practical interpretability.
Journal
Published
LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis
Jesika Debnath, Amira Hossain, Anamul Sakib, Hamdadur Rahman, Rezaul Haque, Md Redwan Ahmed, Ahmed Wasif Reza, SM Masfequier Rahman Swapno, Abhishek Appaji
Informatics in Medicine Unlocked (Elsevier)
Hybrid ViT with attention and XAI for efficient and explainable lung cancer diagnosis (deployment-oriented).
Journal
Published
Accelerated and accurate cervical cancer diagnosis using a novel stacking ensemble method with explainable AI
Md Ismail Hossain Siddiqui, Shakil Khan, Zishad Hossain Limon, Hamdadur Rahman, Mahbub Alam Khan, Abdullah Al Sakib, SM Masfequier Rahman Swapno, Rezaul Haque, Ahmed Wasif Reza, Abhishek Appaji
Informatics in Medicine Unlocked (Elsevier)
Stacking ensemble + explainability for reliable cervical cancer diagnosis using Pap smear imaging.
Journal
Published
Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis
Md Redwan Ahmed, Hamdadur Rahman, Zishad Hossain Limon, Md Ismail Hossain Siddiqui, Mahbub Alam Khan, Al Shahriar Uddin Khondakar Pranta, Rezaul Haque, SM Masfequier Rahman Swapno, Young-Im Cho, Mohamed S Abdallah
Bioengineering (MDPI)
Federation-ready Swin-Transformer ensemble with post-hoc explainability for robust breast cancer diagnosis.
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