Temporal Validation for AI-Based Colorectal Polyp Detection
Evaluating object detection and tracking consistency across continuous endoscopic video sequences. (Ongoing B.Tech final-year research project).
Introduction & Technical Context
Most deep learning models evaluated on medical imaging focus on single-frame static metrics such as per-frame precision, recall, or mAP. However, in real-world colonoscopy procedures, video streams introduce inter-frame camera motion, specular highlights, and momentary occlusions. In my ongoing B.Tech final-year research project, I am investigating temporal validation strategies for AI-based colorectal polyp detection and tracking across continuous video sequences.
1. The Limitation of Static Per-Frame Evaluation
Evaluating video polyp detection on static frame samples treats every frame as an independent image. A detector might achieve high per-frame accuracy on benchmark datasets while flickering on and off every 3 frames during real-time video, creating severe distraction and clinical fatigue for endoscopists. Real clinical deployment requires evaluating temporal stability across consecutive video frames.
2. Framework for Inter-Frame Consistency & Temporal Metrics
Our research framework evaluates spatial detection and segmentation networks alongside inter-frame temporal consistency. By tracking bounding boxes across sequential frame queues, we measure persistence, trajectory smooth loss, and false-positive jitter under continuous camera movement.
import numpy as np
def compute_temporal_stability(frame_detections, IoU_threshold=0.5):
"""
Evaluates tracking continuity across consecutive endoscopic video frames.
"""
stable_tracks = 0
total_sequences = len(frame_detections) - 1
for t in range(total_sequences):
boxes_t = frame_detections[t]
boxes_t1 = frame_detections[t+1]
# Calculate overlap IoU across frame t and frame t+1
iou_matrix = calculate_iou(boxes_t, boxes_t1)
if np.max(iou_matrix) >= IoU_threshold:
stable_tracks += 1
stability_score = stable_tracks / max(1, total_sequences)
return stability_score3. Ongoing Progress & Paper Preparation
Please note: This work represents active, ongoing research for my final-year undergraduate project. We are currently systematically benchmark-testing video tracking sequences, collecting empirical performance logs, and drafting our research paper for eventual peer review.
Key Engineering Takeaways
- Static single-frame metrics alone do not reflect real-time clinical video utility.
- Temporal validation measures track continuity, inter-frame jitter, and detection persistence over time.
- Ongoing research aims to provide clearer empirical benchmarks for video-based medical AI.
Sujan K S — AI/ML Engineer