mlsanityData to 7 Oct 2026

Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation

arXiv 2112.11541 ↗First posted 21 Dec 2021eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain. Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training. Results: Using only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Conclusions: Our results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 21 Dec 2021.

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References · 21

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9 more
  1. 2020Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentationcited by 19
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