Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.Comment: Accepted to MIDL 202

X-Linked Lymphoproliferative Disease Mimicking Multisystem Inflammatory Syndrome in Children—A Case Report

Most children with a SARS-CoV-2 infection are asymptomatic or exhibit mild symptoms. However, a small number of children develop features of substantial inflammation temporarily related to the COVID-19 also called multisystem inflammatory syndrome in children (MIS-C) or pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS), clinically similar to Kawasaki disease, toxic shock syndrome and hemophagocytic lymphohistiocytosis (HLH). It is well-known that genetic pre-disposition plays an important role in virally-triggered diseases such as Epstein-Barr virus (EBV)-associated HLH, while this has not yet been established for patients with MIS-C. Here we describe a male patient fulfilling the diagnostic criteria of MIS-C, who was initially treated according to current consensus guidelines. Presence of hypofibrinogenemia, normal lymphocyte counts and C-reactive protein, but substantial hyperferritinemia distinguish this patient from others with MIS-C. The clinical course following initial presentation with acute respiratory distress syndrome was marked by fatal liver failure in the context of EBV-associated HLH despite treatment with steroids, intravenous immunoglobulins, interleukin (IL)-1 receptor blockade and eventually HLH-directed treatment. X-linked lymphoproliferative disease type 1 (XLP1), a subtype of primary HLH was diagnosed in this patient post-mortem. This case report highlights the importance of including HLH in the differential diagnosis in MIS-C with severe disease course to allow specific, risk-adapted treatment and genetic counseling