FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection

• URL: http://arxiv.org/abs/2510.09583v1
• Date: Fri, 10 Oct 2025 17:38:40 GMT
• Title: FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection
• Authors: Shubham Trehan, Udhav Ramachandran, Akash Rao, Ruth Scimeca, Sathyanarayanan N. Aakur,
• Abstract summary: We present FSP-DETR, a unified detection framework that enables robust few-shot detection, open-set recognition, and generalization to unseen biomedical tasks.<n>Built upon a class-agnostic DETR backbone, our approach constructs class prototypes from original support images and learns an embedding space using augmented views and a lightweight transformer decoder.<n>Tests across ova, blood cell, and malaria detection tasks demonstrate that FSP-DETR significantly outperforms prior few-shot and prototype-based detectors.
• Score: 6.732071883787906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection in biomedical settings is fundamentally constrained by the scarcity of labeled data and the frequent emergence of novel or rare categories. We present FSP-DETR, a unified detection framework that enables robust few-shot detection, open-set recognition, and generalization to unseen biomedical tasks within a single model. Built upon a class-agnostic DETR backbone, our approach constructs class prototypes from original support images and learns an embedding space using augmented views and a lightweight transformer decoder. Training jointly optimizes a prototype matching loss, an alignment-based separation loss, and a KL divergence regularization to improve discriminative feature learning and calibration under scarce supervision. Unlike prior work that tackles these tasks in isolation, FSP-DETR enables inference-time flexibility to support unseen class recognition, background rejection, and cross-task adaptation without retraining. We also introduce a new ova species detection benchmark with 20 parasite classes and establish standardized evaluation protocols. Extensive experiments across ova, blood cell, and malaria detection tasks demonstrate that FSP-DETR significantly outperforms prior few-shot and prototype-based detectors, especially in low-shot and open-set scenarios.

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