CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound

Abstract This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local-and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was funded by NIH. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Pennsylvania State University gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes This work was supported by NIH grant 7R01HD085853NIH, NIH Director’s Transformative Award 1R01AI145057 and NIH Director’s Pioneer Award 5DP1HD086071. S. Schiff and V. Monga shared seniority. (e-mail: ethanyu9668{at}gmail.com, vmonga{at}engr.psu.edu). (email: mpeterson1{at}pennstatehealth.psu.edu, tdh3{at}me.com). (email: kathy.burgoine{at}gmail.com, info{at}kemri-wellcome.org). (email: aweeks{at}liverpool.ac.uk, melglad{at}liverpool.ac.uk). (email: cornelia.hagmann{at}kispi.uzh.ch). (email: f.cowan{at}imperial.ac.uk). (email: sarah.morton{at}childrens.harvard.edu). (ronald.mulondo{at}yale.edu, edith.mbabazikabachelor{at}yale.edu, steven.schiff{at}yale.edu). Data Availability All data produced in the present study are available upon reasonable request to the authors