# Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN

arXiv: https://arxiv.org/abs/1707.06053 · HTML: https://mlsanity.com/paper/1707.06053/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge](https://mlsanity.com/paper/2102.02711/) (2021)
- [PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification](https://mlsanity.com/paper/2102.05917/) (2021)

## Abstract (arXiv)

Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into sub-categories of boundary and interior patches, the decisions of which are fused to reach a binary lesion vs non-lesion decision. For validation of our system, we use CT images of 132 livers and 498 lesions. Our approach shows highly improved detection results that outperform the state-of-the-art fully convolutional network. Automated computerized tools, as shown in this work, have the potential in the future to support the radiologists towards improved detection.