3 accepted papers
Disease screening is critical for early detection and timely intervention in clinical practice. However, current screening models for medical images often suffer from limited interpretability and suboptimal performance, as they lack mechanisms to reference historical cases or provide transparent rea…
Modern studies in radiograph representation learning (R$^2$L) rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-c…
Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field of medical image-to-image translation. However, neither modes are ideal. The Pix2Pix mode has excellent performance. But it requires paired and well pixel-wise aligned images, which may not always be achievab…