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An Expert-Guided Multimodal AI Ecosystem for Diagnostic Intelligence

by Healthcare

An Expert-Guided Multimodal AI Ecosystem for Diagnostic Intelligence

An Expert-Guided Multimodal AI Ecosystem for Diagnostic Intelligence is designed to tackle one of the most persistent challenges in modern radiology: the lack of fully integrated, adaptable, and interpretable AI systems for medical imaging. While deep learning models for tumor segmentation and natural language processing tools for report generation exist, they are often static, siloed, and difficult to translate into clinical workflows. This project aims to unify these pieces into a seamless pipeline that not only segments multi-modal MRI scans but also generates expert-level diagnostic reports, guided by continual learning and explainability. By creating an end-to-end ecosystem, the work bridges the gap between research prototypes and tools that radiologists can actually use in practice.

At the technical core of the project is the Mixture of Modality Experts (MoME+) segmentation model, enhanced with continual learning strategies such as Elastic Weight Consolidation and Replay Memory. This allows the system to adapt as new datasets or imaging modalities are introduced, avoiding the common pitfall of catastrophic forgetting. Segmentation outputs are then passed into a domain-adapted large language model (LLM) trained on synthetic and expert-reviewed clinical data. The LLM interprets tumor masks, extracts structured findings, and generates human-readable diagnostic reports. To ensure transparency, explainability methods like Grad-CAM overlays are integrated, allowing clinicians to see exactly where and how the model focused during segmentation.

The ecosystem is delivered through a modular, web-based platform built with a modern backend (Django, Flask, PostgreSQL) and a responsive frontend (React, Tailwind). Clinicians can upload MRI scans, visualize segmentation outputs, and instantly receive a draft diagnostic report, all within a secure and user-friendly interface. Deployed on cloud infrastructure and optimized for real-time inference, the system is designed to scale for use in hospitals, telemedicine services, and research institutions. The project contributes scientifically by advancing continual learning in medical imaging and by demonstrating how multimodal AI can be packaged into an interpretable, end-to-end diagnostic tool. In the long run, it offers a pathway toward more accessible, efficient, and intelligent radiology support systems that can reduce workload pressures while improving diagnostic consistency.

Faculty

Students

  • Ahmed Sultan
  • Farhan Kashif
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Analyzing UAV-Based Multispectral Data for Genotypic-Aware Crop Performance Assessment

by Precision Agriculture

Analyzing UAV-Based Multispectral Data for Genotypic-Aware Crop Performance Assessment

What if we rethink how crop monitoring is done in experimental and breeding trials? Instead of treating entire research fields as uniform blocks, this work drills down to the level of individual genotypes, using UAV-mounted multispectral sensors to capture detailed spectral and morphological traits across hundreds of wheat plots. Traditional monitoring methods often mask critical differences among varieties, making it hard for breeders to identify which lines truly outperform others under diverse environmental conditions. By integrating UAV imagery with advanced trait extraction, the project creates a pipeline that can evaluate crop health and performance with unprecedented granularity.

We combine UAV-based remote sensing with statistical filtering and machine learning models. Over the course of a full growing season, drone flights captured temporal reflectance data from wheat plots, covering more than 100 genotypes. From this imagery, vegetation indices and morphological traits are extracted and analyzed to reveal genotype-specific differences in growth patterns and stress responses. These traits are then tested for yield relevance using statistical models, followed by the development of machine learning regressors and deep learning architectures (LSTMs, temporal CNNs) for yield prediction. By merging feature-driven and image-driven approaches, the system identifies which traits matter most and also learns growth dynamics directly from spectral time series.

The impact of this work lies in its ability to accelerate breeding decisions and improve agricultural resilience. The integrated decision-support platform allows breeders to visualize predicted yields, assess trait-level contributions for each genotype, and make informed choices on which varieties to advance. By reducing reliance on manual ground-truthing and shifting toward automated UAV-based data collection, the approach lowers labor costs while scaling to large breeding programs. The insights generated can help pinpoint stress-tolerant, high-yielding genotypes, guide efficient resource management, and ultimately contribute to global food security. Moreover, the framework is designed to be scalable and adaptable, paving the way for genotype-aware monitoring in other staple crops.

Faculty

Students

  • Muhammad Tayyab Iftikhar
  • Muhammad Hissan Umar
  • Abdullah Imran