Published on February 21, 2022–Updated on October 24, 2022
Dates
on the March 4, 2022
from 10:30am to 12:00pm
Program
10:30 - 11:00
Hind Dadoun (Inria)
AI-based Real Time Diagnosis of Abdominal Ultrasound Images
Abstract:Ultrasound (US) imaging is one of the most common techniques for medical diagnosis. It is the only non-invasive imaging modality, with no side effects (such as radiation-induced cancers) that can be used in real-time, making it a method of choice: whether in an emergency, in consultation for patient follow-up or during a public health screening examination. However, acquiring and interpreting an ultrasound image is a difficult and examiner-dependent task with a limited number of trained operators. As a result, despite the decrease in ultrasound hardware prices, limitations to its use persist. The focus of our study is to analyze how machine learning tools can be used for the automatic interpretation of abdominal ultrasound images, with a major setback : the absence of curated, annotated and openly available abdominal US databases. In this presentation, we will detail those challenges and point out first elements to alleviate some of them.
11:00 - 11:30
Victor Jung (UCA)
Checking Constraint Satisfaction
Abstract:We address the problem of verifying a constraint C by a set of solutions S in the context of Constraint Programming. This problem is present in almost all systems aiming at learning or acquiring constraints or constraint parameters. We propose an original approach based on MDDs. Indeed, the set of solutions can be represented by the MDD denoted by MDD(S). Checking whether S satisfies a given constraint C can be done using MDD(C), the MDD that contains the set of solutions of C, and by searching if the intersection between MDD(S) and MDD(C) is equal to MDD(S). This step is equivalent to searching whether MDD(S) is included in MDD(C). We show that this approach can be generalized for the computation of global constraint parameters satisfying C. Next, we show that the introduction of node information allows us to define a new algorithm capable to compute in only one step the set of parameters we are looking for. Finally, we present experimental results showing the interest of our approach.
