# Ultrasound Detection of Subquadricipital Recess Distension

arXiv: https://arxiv.org/abs/2211.12089 · HTML: https://mlsanity.com/paper/2211.12089/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 66 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation](https://mlsanity.com/paper/1908.04373/) (2019)
- [A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging](https://mlsanity.com/paper/1906.01828/) (2019)
- [Multitask Classification and Segmentation for Cancer Diagnosis in Mammography](https://mlsanity.com/paper/1909.05397/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images](https://mlsanity.com/paper/2305.18489/) (2023)

## Abstract (arXiv)

Joint bleeding is a common condition for people with hemophilia and, if untreated, can result in hemophilic arthropathy. Ultrasound imaging has recently emerged as an effective tool to diagnose joint recess distension caused by joint bleeding. However, no computer-aided diagnosis tool exists to support the practitioner in the diagnosis process. This paper addresses the problem of automatically detecting the recess and assessing whether it is distended in knee ultrasound images collected in patients with hemophilia. After framing the problem, we propose two different approaches: the first one adopts a one-stage object detection algorithm, while the second one is a multi-task approach with a classification and a detection branch. The experimental evaluation, conducted with 483 annotated images, shows that the solution based on object detection alone has a balanced accuracy score of 0.74 with a mean IoU value of 0.66, while the multi-task approach has a higher balanced accuracy value (0.78) at the cost of a slightly lower mean IoU value.