AIMC Topic: Image Interpretation, Computer-Assisted

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Enhancing nuclei segmentation in breast histopathology images using U-Net with backbone architectures.

Computers in biology and medicine
Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for accurate and timely diagnostic methods. Precise segmentation of nuclei in breast histopathology images is crucial for effective diagnosis and prognosi...

A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation.

Computers in biology and medicine
BACKGROUND: Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpret...

Deep pixel-wise supervision for skin lesion classification.

Computers in biology and medicine
BACKGROUND: Utilizing automated systems for diagnosing malignant skin lesions promises to improve the early detection of skin diseases and increase patients' survival rates. However, current classification methods primarily focus on global features, ...

AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes.

Medical image analysis
Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing gigapixel-resolution images with slide-level labels. Recent attention-based MIL architectures have significa...

REPAIR: Reciprocal assistance imputation-representation learning for glioma diagnosis with incomplete MRI sequences.

Medical image analysis
The absence of MRI sequences is a common occurrence in clinical practice, posing a significant challenge for prediction modeling of non-invasive diagnosis of glioma (GM) via fusion of multi-sequence MRI. To address this issue, we propose a novel unif...

SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images.

Medical image analysis
Quality control (QC) of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development by enhancing network performance in semi-supervised and active l...

AdaptFRCNet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation.

Medical image analysis
Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the application of LPM in the medica...

Exploring multi-instance learning in whole slide imaging: Current and future perspectives.

Pathology, research and practice
Whole slide images (WSI), due to their gigabyte-scale size and ultra-high resolution, play a significant role in diagnostic pathology. However, the enormous data size makes it difficult to directly input these images into image processing units (GPU)...

Establishment of an intelligent analysis system for clinical image features of melanonychia based on deep learning image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Melanonychia, a condition that can be indicative of malignant melanoma, presents a significant challenge in early diagnosis due to the invasive nature and equipment dependency of traditional diagnostic methods such as nail biopsy and dermatoscope ima...