AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 391 to 400 of 3033 articles

Entity-level multiple instance learning for mesoscopic histopathology images classification with Bayesian collaborative learning and pathological prior transfer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Entity-level pathologic structures with independent structures and functions are at a mesoscopic scale between the cell-level and slide-level, containing limited structures thus providing fewer instances for multiple instance learning. Th...

SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pathology image classification is crucial in clinical cancer diagnosis and computer-aided diagnosis. Whole Slide Image (WSI) classification is often framed as a multiple instance learning (MIL) problem due to the high cost o...

LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation.

Methods (San Diego, Calif.)
In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy ...

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Computers in biology and medicine
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmenta...

Application-driven validation of posteriors in inverse problems.

Medical image analysis
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models and Invertible...

Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval.

Medical image analysis
Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular...

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.

BMC medical imaging
Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the effic...

Enhanced accuracy and stability in automated intra-pancreatic fat deposition monitoring of type 2 diabetes mellitus using Dixon MRI and deep learning.

Abdominal radiology (New York)
PURPOSE: Intra-pancreatic fat deposition (IPFD) is closely associated with the onset and progression of type 2 diabetes mellitus (T2DM). We aimed to develop an accurate and automated method for assessing IPFD on multi-echo Dixon MRI.