Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022.

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning.

Self-supervised learning (SSL) is potentially useful in reducing the need for manual annotation and making deep learning models accessible for medical image analysis tasks. By leveraging the representations learned from unlabeled data, self-supervised models perform well on tasks that require little to no fine-tuning. However, for medical images, like chest X-rays, characterized by complex anatomical structures and diverse clinical conditions, a need arises for representation learning techniques that encode fine-grained details while preserving the broader contextual information.

RETHINKING INTERMEDIATE LAYERS DESIGN IN KNOWLEDGE DISTILLATION FOR KIDNEY AND LIVER TUMOR SEGMENTATION.

Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, has encountered challenges. Many existing KD methods are not specifically tailored for these tasks. Moreover, prevalent KD methods often lack a careful consideration of 'what' and 'from where' to distill knowledge from the teacher to the student.

Large Scale Time-Series Representation Learning via Simultaneous Low-and High-Frequency Feature Bootstrapping.

Learning representations from unlabeled time series data is a challenging problem. Most existing self-supervised and unsupervised approaches in the time-series domain fall short in capturing low-and high-frequency features at the same time. As a result, the generalization ability of the learned representations remains limited.

PaCL: Patient-aware contrastive learning through metadata refinement for generalized early disease diagnosis.

Early diagnosis plays a pivotal role in effectively treating numerous diseases, especially in healthcare scenarios where prompt and accurate diagnoses are essential. Contrastive learning (CL) has emerged as a promising approach for medical tasks, offering advantages over traditional supervised learning methods. However, in healthcare, patient metadata contains valuable clinical information that can enhance representations, yet existing CL methods often overlook this data.