Deep Clustering for Abdominal Organ Classification in US imaging - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Medical Imaging Year : 2023

Deep Clustering for Abdominal Organ Classification in US imaging


The use of ultrasound (US) imaging has developed considerably in several medical specialties recently. In particular, abdominal pain accounts for a significant part of medical consultations. In this context, ultrasound is the only non-invasive and non-ionizing imaging modality that allows real-time medical exploration of a specific body part. However, acquiring and interpreting US images remains a difficult and examiner-dependent task, with a limited number of trained operators. For abdominal organs, ultrasound images are even more difficult to interpret because some of the organs of interest are located deep inside the body and patient-related factors, such as the presence of fatty tissue, can hinder the reading. In this work, we present a simple framework for abdominal organ clustering using unlabeled ultrasound images. This method can serve as a tool to preprocess large uncurated databases, reducing the need for annotation in abdominal ultrasound studies. When few labeled examples are available, we explore how unlabeled data can be leveraged to improve the performance of multi-label classification as opposed to the traditional transfer learning approach. In particular, we show that for supervised fine-tuning, deep clustering is an effective pretraining method, with performance matching that of ImageNet pre-training using five times less labeled data. Finally, we combine this pre-training method with semi-supervised learning and report the performances.
Fichier principal
Vignette du fichier
soumission_jmi_dadoun_vf.pdf (2.79 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03773082 , version 1 (08-09-2022)
hal-03773082 , version 2 (12-09-2022)
hal-03773082 , version 3 (13-09-2022)


  • HAL Id : hal-03773082 , version 3


Hind Dadoun, Hervé Delingette, Anne-Laure Rousseau, Eric de Kerviler, Nicholas Ayache. Deep Clustering for Abdominal Organ Classification in US imaging. Journal of Medical Imaging, In press. ⟨hal-03773082v3⟩
101 View
88 Download


Gmail Facebook Twitter LinkedIn More