Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data

TL;DR

This paper introduces MTHD, a semi-supervised lesion detection method that enforces consistency across different detector outputs and handles missing sequences in multi-sequence data, significantly improving detection performance.

Contribution

MTHD is the first to formulate a mean teacher approach for hetero-modal detection without compromises, incorporating expansive consistency constraints for semi-supervised learning.

Findings

MTHD outperforms fully-supervised methods by 10.1% in sensitivity.

MTHD surpasses semi-supervised competitors by 3.5% in sensitivity.

Uses the largest MR lesion dataset to date for evaluation.

Abstract

Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the high costs of annotations hinder the collection of large and completely labeled datasets, motivating semi-supervised detection approaches. In this paper, we introduce mean teacher hetero-modal detection (MTHD), which addresses two important gaps in current semi-supervised detection. First, it is not obvious how to enforce unlabeled consistency constraints across the very different outputs of various detectors, which has resulted in various compromises being used in the state of the art. Using an anchor-free framework, MTHD formulates a mean teacher approach without such compromises, enforcing consistency on the soft-output of object centers and size. Second, multi-sequence data is often critical, e.g., for abdominal lesion detection, but unlabeled data is often missing sequences. To deal with this, MTHD incorporates hetero-modal learning in its framework. Unlike prior art, MTHD is able to incorporate an expansive set of consistency constraints that include geometric transforms and random sequence combinations. We train and evaluate MTHD on liver lesion detection using the largest MR lesion dataset to date (1099 patients with >5000 volumes). MTHD surpasses the best fully-supervised and semi-supervised competitors by 10.1% and 3.5%, respectively, in average sensitivity.