Tissue concepts: Supervised foundation models in computational pathology.

Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Foundation models have the potential to improve generalizability within and across centers and serve as starting points for data efficient development of specialized yet robust AI models. However, the training of foundation models themselves is usually very expensive in terms of data, computation, and time.

Medical Image Segmentation Review: The Success of U-Net.

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model has received tremendous attention from academic and industrial researchers who have extended it to address the scale and complexity created by medical tasks.

Refining pain management in mice by comparing multimodal analgesia and NSAID monotherapy for neurosurgical procedures.

While neurosurgical interventions are frequently used in laboratory mice, refinement efforts to optimize analgesic management based on multimodal approaches appear to be rather limited. Therefore, we compared the efficacy and tolerability of combinations of the non-steroidal anti-inflammatory drug carprofen, a sustained-release formulation of the opioid buprenorphine, and the local anesthetic bupivacaine with carprofen monotherapy. Female and male C57BL/6J mice were subjected to isoflurane anesthesia and an intracranial electrode implant procedure.

Overcoming data scarcity in biomedical imaging with a foundational multi-task model.

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements.

Ultra high frequency ultrasound enables real-time visualization of blood supply from chorioallantoic membrane to human autosomal dominant polycystic kidney tissue.

Ultra high frequency (UHF) ultrasound enables the visualization of very small structures that cannot be detected by conventional ultrasound. The utilization of UHF imaging as a new imaging technique for the 3D-in-vivo chorioallantoic membrane (CAM) model can facilitate new insights into tissue perfusion and survival. Therefore, human renal cystic tissue was grafted onto the CAM and examined using UHF ultrasound imaging.