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

03/24/2021
by   Bolin Lai, et al.
0

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 respectively, in average sensitivity.

READ FULL TEXT

page 3

page 13

research
02/22/2022

A Semi-Supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues

Identifying breakdowns in ongoing dialogues helps to improve communicati...
research
06/19/2022

Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors

With the recent development of Semi-Supervised Object Detection (SS-OD) ...
research
07/16/2019

Semi-supervised Breast Lesion Detection in Ultrasound Video Based on Temporal Coherence

Breast lesion detection in ultrasound video is critical for computer-aid...
research
03/04/2019

Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

Automated brain lesion segmentation provides valuable information for th...
research
05/19/2020

A Self-ensembling Framework for Semi-supervised Knee Osteoarthritis Localization and Classification with Dual-Consistency

Knee osteoarthritis (OA) is one of the most common musculoskeletal disor...
research
08/10/2021

Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

Deep learning models achieve strong performance for radiology image clas...
research
09/02/2020

Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training

In this work, we propose a novel single-shot and keypoints-based framewo...

Please sign up or login with your details

Forgot password? Click here to reset