Medical image segmentation using deep learning
Image segmentation plays an essential role in medical image analysis as it provides
automated delineation of specific anatomical structures of interest and further enables many …
automated delineation of specific anatomical structures of interest and further enables many …
[HTML][HTML] Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of
radiation to tumors while sparing healthy tissues over multiple days. Computed tomography …
radiation to tumors while sparing healthy tissues over multiple days. Computed tomography …
Enhancing modality-agnostic representations via meta-learning for brain tumor segmentation
In medical vision, different imaging modalities provide complementary information. However,
in practice, not all modalities may be available during inference or even training. Previous …
in practice, not all modalities may be available during inference or even training. Previous …
Mmcformer: Missing modality compensation transformer for brain tumor segmentation
S Karimijafarbigloo, R Azad… - … Imaging with Deep …, 2024 - proceedings.mlr.press
Human brain tumours and more specifically gliomas are amongst the most life-threatening
cancers which usually arise from abnormal growth of the glial stem cells. In practice …
cancers which usually arise from abnormal growth of the glial stem cells. In practice …
Modal-aware visual prompting for incomplete multi-modal brain tumor segmentation
In the realm of medical imaging, distinct magnetic resonance imaging (MRI) modalities can
provide complementary medical insights. However, it is not uncommon for one or more …
provide complementary medical insights. However, it is not uncommon for one or more …
Omni-seg: A scale-aware dynamic network for renal pathological image segmentation
Comprehensive semantic segmentation on renal pathological images is challenging due to
the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the …
the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the …
A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI
Diffusion-weighted (DW) MRI measures the direction and scale of the local diffusion process
in every voxel through its spectrum in q-space, typically acquired in one or more shells …
in every voxel through its spectrum in q-space, typically acquired in one or more shells …
Deep Learning–based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets
Purpose To determine whether the unsupervised domain adaptation (UDA) method with
generated images improves the performance of a supervised learning (SL) model for …
generated images improves the performance of a supervised learning (SL) model for …
Does Adding a Modality Really Make a Positive Impacts in Incomplete Multi-modal Brain Tumor Segmentation?
Previous incomplete multi-modal brain tumor segmentation technologies, while effective in
integrating diverse modalities, commonly deliver under-expected performance gains. The …
integrating diverse modalities, commonly deliver under-expected performance gains. The …
Learning site-specific styles for multi-institutional unsupervised cross-modality domain adaptation
Unsupervised cross-modality domain adaptation is a challenging task in medical image
analysis, and it becomes more challenging when source and target domain data are …
analysis, and it becomes more challenging when source and target domain data are …