DetectIQ

AI for Early Cancer Detection (Computer Vision) Location: On-site — Kochi, Kerala, India Employment: Full-time (6–12 month contract with potential extension) Start date: Immediate ## About the project We are building a pilot AI system for early cancer detection using advanced computer vision on medical imaging. The goal is to deliver a research-grade prototype that detects and prioritizes clinically relevant findings and integrates with standardized reporting workflows, paving the way for a prospective clinical study. ## Role overview The Junior Researcher will work on-site and implement an end-to-end pipeline: data curation, preprocessing/DICOM handling, anatomical segmentation, classical candidate generation, 3D deep learning–based detection, evaluation, and a lightweight viewer for reader-in-the-loop assessment. Close mentorship and clear milestones are provided. ## Key responsibilities - Data curation - Ingest and organize de-identified imaging studies and associated labels; maintain manifests, provenance, and documentation. - Structure labels into clinically meaningful size/risk bins; handle noisy/incomplete annotations. - Algorithm development - Preprocess volumetric CT data (resampling, denoising, normalization; multi-position alignment where applicable). - Implement segmentation of relevant anatomy and classical candidate generation to reduce false positives. - Train a 3D CNN detector on candidate-centered patches with weak/semi-supervised targets; optimize sensitivity for clinically significant findings while controlling false positives per case. - Optional: add a characterization head (e.g., risk/biologic likelihood) where reliable ground truth exists. - Evaluation and reporting - Define robust patient-level splits; compute per-lesion sensitivity by size/risk, false positives per case, and per-case sensitivity. - Build simple visualization/overlays and generate standardized, clinically aligned summaries for internal review. - Document methods, code, and results; contribute to an internal white paper and potential abstract. ## Must-have qualifications - B.E./B.Tech/M.Sc./M.Tech in Computer Science, Biomedical Engineering, Data Science, or related fields. - Hands-on experience with Python, PyTorch/TensorFlow, and medical imaging toolkits (e.g., MONAI, SimpleITK, pydicom). - Practical knowledge of 3D CNNs/UNet variants, volumetric data pipelines, and GPU training workflows. - Demonstrated project in medical imaging or volumetric detection/segmentation (GitHub/portfolio or paper). - Strong experimentation hygiene: Git, reproducible environments, and clear documentation. - Willingness and ability to work on-site in Kochi, Kerala. ## Nice-to-have - Experience with CT imaging, anatomical segmentation, or classical computer vision (e.g., curvature-based candidate generation, artifact suppression). - Familiarity with radiomics and weak/semi-supervised learning for noisy labels. - Understanding of clinical reporting frameworks and screening metrics. - Experience building simple viewers (Streamlit/Gradio/ITK widgets) and robust DICOM handling. - Exposure to data governance for clinical datasets and basic biostatistics. ## What success looks like in 12 weeks - Curated and versioned imaging subsets ready for training, with label confidence tracking. - Baseline candidate generator with segmentation and measurable false-positive reduction. - 3D detection model achieving high sensitivity for clinically significant findings with controlled false positives per case. - Clear evaluation on a held-out test set and a lightweight reader-in-the-loop demo. ## Tools and stack - Python, PyTorch, MONAI, SimpleITK/pydicom, NumPy/Pandas - Experiment tracking (Weights & Biases/MLflow), Docker/conda, Git - Optional: Streamlit/Gradio for viewer, DICOMweb utilities ## What we offer - Exposure to translational research with potential publication/abstract opportunities. - Collaborative lab environment, competitive stipend, and performance-based extension. - Opportunity to impact real-world healthcare workflows. ## How to apply Email a brief note, CV, and links to relevant projects (GitHub/portfolio/papers) with subject “Junior Researcher – On-site (Kochi) – AI for Early Cancer Detection” to admin@detectiq.net Include: - A short paragraph on experience with 3D medical imaging. - One example of handling noisy or incomplete labels. - Availability and preferred start date. - Confirmation of on-site availability in Kochi. ## Application deadline Rolling review; priority for applications received within 2 weeks. Note: Prior domain-specific experience is a plus but not mandatory—strong fundamentals, curiosity, and grit matter most.