# GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

## TL;DR

This paper introduces GP-Unet, a 3D regression neural network that detects lesions in medical images using only global lesion count labels, enabling effective localization without detailed annotations.

## Contribution

The novel GP-Unet architecture allows lesion detection from weak labels by combining regression and localization in a fully convolutional network.

## Key findings

- Achieves 62% sensitivity in lesion detection
- Outperforms thresholding and saliency-based methods by 20%
- Operates effectively with only global lesion count labels

## Abstract

We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.