# Medical Image Segmentation via Unsupervised Convolutional Neural Network

arXiv: https://arxiv.org/abs/2001.10155 · HTML: https://mlsanity.com/paper/2001.10155/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor](https://mlsanity.com/paper/2306.14505/) (2023)
- [Self-Supervised Endoscopic Image Key-Points Matching](https://mlsanity.com/paper/2208.11424/) (2022)
- [Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks](https://mlsanity.com/paper/2104.08623/) (2021)
- [A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2306.05476/) (2023)
- [Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification](https://mlsanity.com/paper/2502.19258/) (2025)
- [Out-of-distribution data supervision towards biomedical semantic segmentation](https://mlsanity.com/paper/2507.12105/) (2025)

## Abstract (arXiv)

For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required. In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised. Specifically, in the unsupervised setting, we parameterize the Active contour without edges (ACWE) framework via a convolutional neural network (ConvNet), and optimize the parameters of the ConvNet using a self-supervised method. In another setting (semi-supervised), the auxiliary segmentation ground truth is used during training. We show that the method provides fast and high-quality bone segmentation in the context of single-photon emission computed tomography (SPECT) image.