Few-Shot Domain Adaptive Object Detection for Microscopic Images

TL;DR

This paper introduces a novel few-shot domain adaptive object detection method tailored for microscopic images, addressing class imbalance and limited data challenges to improve detection accuracy across domains.

Contribution

The paper proposes a domain adaptive class balancing strategy, multi-layer instance-level alignment, and an instance-level classification loss for improved microscopic image detection.

Findings

Achieves state-of-the-art results on two public microscopic datasets.

Effectively handles class imbalance and domain shift in microscopic images.

Demonstrates significant improvements over baseline methods.

Abstract

In recent years, numerous domain adaptive strategies have been proposed to help deep learning models overcome the challenges posed by domain shift. However, even unsupervised domain adaptive strategies still require a large amount of target data. Medical imaging datasets are often characterized by class imbalance and scarcity of labeled and unlabeled data. Few-shot domain adaptive object detection (FSDAOD) addresses the challenge of adapting object detectors to target domains with limited labeled data. Existing works struggle with randomly selected target domain images that may not accurately represent the real population, resulting in overfitting to small validation sets and poor generalization to larger test sets. Medical datasets exhibit high class imbalance and background similarity, leading to increased false positives and lower mean Average Precision (map) in target domains. To overcome these challenges, we propose a novel FSDAOD strategy for microscopic imaging. Our contributions include a domain adaptive class balancing strategy for few-shot scenarios, multi-layer instance-level inter and intra-domain alignment to enhance similarity between class instances regardless of domain, and an instance-level classification loss applied in the middle layers of the object detector to enforce feature retention necessary for correct classification across domains. Extensive experimental results with competitive baselines demonstrate the effectiveness of our approach, achieving state-of-the-art results on two public microscopic datasets. Code available at https://github.co/intelligentMachinesLab/few-shot-domain-adaptive-microscopy