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🧠 Towards AI-Driven Cancer Diagnostics: A Hive-of-Agents Approach by BhuviAI

BhuviAI explores the next frontier in cancer diagnostics through an Agentic AI framework — a collaborative “Hive-of-Agents” ecosystem that automates pathology, radiology, and genomic analysis. The article introduces OncoDx, an open, GPU-accelerated platform that integrates data understanding, model selection, and adaptive training agents to enable faster, explainable, and clinician-augmented cancer diagnostics.

BHUVIAI LAB

By Dr. Praveen · Rajeev Sharma (Clinical Advisor & Founder – BhuviAI Solutions LLP)

10/15/20252 min read

The Challenge

Cancer diagnosis remains one of the most data-intensive and cognitively demanding tasks in modern medicine. Despite the proliferation of digital pathology, high-resolution whole-slide imaging, and AI-powered analysis tools, diagnostic workflows still rely heavily on human interpretation — often across disconnected software systems, inconsistent annotations, and repetitive validation cycles.

This fragmentation delays reporting, adds workload on pathologists, and limits scalability across institutions. True digital transformation in oncology demands more than just deep learning models — it needs collaborative intelligence that can reason, learn, and act autonomously across modalities.

The Vision: A Hive of Agents

At BhuviAI, we are advancing the idea of a Hive-of-Agents — a cognitive, agent-driven ecosystem for oncology diagnostics where multiple specialized AI agents collaborate to deliver end-to-end automation, traceability, and insight.

Each agent operates with defined autonomy yet remains contextually aware of the collective diagnostic goal:

DUA – Data Understanding Agent
Harmonizes multimodal inputs (WSI, DICOM, genomic, and EHR data) and ensures semantic consistency across diverse formats.
MSA – Model Selection Agent
Dynamically evaluates deep learning models (CNN, ViT, or hybrid architectures) and optimizes inference pathways for accuracy and speed.
TEA – Training & Evaluation Agent
Automates supervised learning pipelines using real pathologist feedback, adaptive annotation, and cross-dataset validation.

Together, these agents form an orchestrated swarm intelligence — continuously improving the diagnostic process through reasoning, verification, and self-learning feedback loops.

Why “Agentic” Matters

Traditional AI systems perform tasks in isolation. Agentic AI, in contrast, enables autonomous decision-making, goal redefinition, and collaboration between models. Each agent can ask questions, test hypotheses, or invoke a peer model to validate findings.

This transforms cancer diagnostics from static automation into adaptive cognition — an evolving intelligence capable of supporting clinicians rather than simply executing algorithms.

The Technical Foundation

The OncoDx initiative at BhuviAI embodies this vision through a fully open, interoperable stack:

Open-Source Base – Built upon tools like 3D Slicer, QuPath, and MONAI, ensuring transparency and community scalability.
GPU-Accelerated Processing – Leveraging CUDA 12.x on RTX 4060 hardware for real-time tile extraction, patch classification, and inference.
Agent-Integrated Annotation – Semi-automated labeling pipelines connecting Slicer and QuPath, enabling high-fidelity tissue segmentation.
Hybrid Compute Architecture – Combining local GPU compute with cloud-based orchestration for scalable experimentation.
Explainability by Design – Integrated visual reasoning layers to make AI decisions interpretable for clinicians and regulators.

This architecture is designed to evolve — from research to deployment — enabling hospitals, labs, and cancer institutes to integrate agentic diagnostics without abandoning their existing workflows.

Early Outcomes and Road Ahead

The first working prototype of OncoDx has successfully processed benchmark datasets such as CAMELYON17 (histopathology) and LIDC (radiomics) under GPU-enabled training environments.
Initial results demonstrate:

Significant reduction in annotation time through agent-guided labeling.
Improved model generalization across mixed data cohorts.
Enhanced traceability and explainability within diagnostic pipelines.

The next phase focuses on integrating multi-modal evidence aggregation — combining histopathology, imaging, and genomic cues into a unified reasoning layer, powered by the Hive-of-Agents engine.

Towards Collaborative Oncology Intelligence

The future of cancer diagnostics lies not in replacing clinicians but in augmenting them with distributed cognitive intelligence.
By allowing specialized AI agents to reason collectively — much like cells in a living system — we can accelerate discovery, reduce diagnostic latency, and deliver precision at scale.

“In oncology, time is tissue — and intelligence is collaboration.”

At BhuviAI, we believe this synergy defines the next frontier of healthcare automation — AI that learns, collaborates, and heals.

About BhuviAI

BhuviAI Solutions LLP is a DPIIT-recognized Indian startup pioneering Agentic Artificial Intelligence across healthcare, telecom, and smart infrastructure domains.
The OncoDx initiative represents its flagship research effort in AI-assisted cancer diagnostics, combining open-source innovation with clinical insight to create scalable, ethical, and explainable diagnostic intelligence. It is innovation initiative of BhuviAI Lab.

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