Reducing annotation burden in MR: A novel MR-contrast guided contrastive learning approach for image segmentation.

Background: Contrastive learning, a successful form of representational learning, has shown promising results in pretraining deep learning (DL) models for downstream tasks. When working with limited annotation data, as in medical image segmentation tasks, learning domain-specific local representations can further improve the performance of DL models.

Purpose: In this work, we extend the contrastive learning framework to utilize domain-specific contrast information from unlabeled Magnetic Resonance (MR) images to improve the performance of downstream MR image segmentation tasks in the presence of limited labeled data.

Long-Term Follow-up of High-Dose Chemotherapy with Autologous Stem Cell Transplantation in Children and Young Adults with Metastatic or Relapsed Ewing Sarcoma: A Single-Institution Experience.

Forty-seven patients with metastatic disease at diagnosis or recurrent Ewing sarcoma (EWS) received high-dose chemotherapy (HDC) followed by tandem (n = 20, from February 13, 1997, to October 24, 2002) or single (n = 27, from October 1, 2004, to September 5, 2018) autologous hematopoietic stem cell transplantation (ASCT). To our knowledge, this is the largest single-institution study with sustained long-term follow-up exceeding 10 years. All patients who underwent single ASCT received a novel conditioning regimen with busulfan, melphalan, and topotecan.