# Two Approaches to Supervised Image Segmentation

arXiv: https://arxiv.org/abs/2307.10123 · HTML: https://mlsanity.com/paper/2307.10123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 94 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [On hallucinations in tomographic image reconstruction](https://mlsanity.com/paper/2012.00646/) (2020)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection](https://mlsanity.com/paper/2301.07779/) (2023)
- [The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems](https://mlsanity.com/paper/2001.01258/) (2020)
- [Further Generalizations of the Jaccard Index](https://mlsanity.com/paper/2110.09619/) (2021)
- [Improving Interpretability of Deep Neural Networks with Semantic Information](https://mlsanity.com/paper/1703.04096/) (2017)
- [Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/1505.02000/) (2015)
- [On Similarity](https://mlsanity.com/paper/2111.02803/) (2021)
- [An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood Vessels](https://mlsanity.com/paper/2111.10255/) (2021)
- [Multiset Neurons](https://mlsanity.com/paper/2111.08516/) (2021)
- [Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation](https://mlsanity.com/paper/1712.10207/) (2017)
- [Multiset Signal Processing and Electronics](https://mlsanity.com/paper/2111.08514/) (2021)
- [Neuromorphic Networks as Revealed by Features Similarity](https://mlsanity.com/paper/2207.10571/) (2022)

## Abstract (arXiv)

Though performed almost effortlessly by humans, segmenting 2D gray-scale or color images into respective regions of interest (e.g.~background, objects, or portions of objects) constitutes one of the greatest challenges in science and technology as a consequence of several effects including dimensionality reduction(3D to 2D), noise, reflections, shades, and occlusions, among many other possibilities. While a large number of interesting related approaches have been suggested along the last decades, it was mainly thanks to the recent development of deep learning that more effective and general solutions have been obtained, currently constituting the basic comparison reference for this type of operation. Also developed recently, a multiset-based methodology has been described that is capable of encouraging image segmentation performance combining spatial accuracy, stability, and robustness while requiring little computational resources (hardware and/or training and recognition time). The interesting features of the multiset neurons methodology mostly follow from the enhanced selectivity and sensitivity, as well as good robustness to data perturbations and outliers, allowed by the coincidence similarity index on which the multiset approach to supervised image segmentation is founded. After describing the deep learning and multiset neurons approaches, the present work develops comparison experiments between them which are primarily aimed at illustrating their respective main interesting features when applied to the adopted specific type of data and parameter configurations. While the deep learning approach confirmed its potential for performing image segmentation, the alternative multiset methodology allowed for enhanced accuracy while requiring little computational resources.