Search Results for author:
Found 11 papers, 3 papers with code
LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model
Additionally, we introduce LHRS-Bench, a benchmark for thoroughly evaluating MLLMs' abilities in RS image understanding.
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex.
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
Learning under Concept Drift: A Review
To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted.
Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data
The presence of basal cells is the most accepted biomarker for benign glandular tissue and the absence of basal cells is a strong indicator of acinar prostatic adenocarcinoma, the most common form of prostate cancer.
A Deep Learning Framework for Automatic Diagnosis in Lung Cancer
The performance of the developed deep learning framework was evaluated on fully annotated TMA cores from 178 patients reaching pixel-wise precision of 0. 80 and recall of 0. 86.
Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images
Digital pathology provides an excellent opportunity for applying fully convolutional networks (FCNs) to tasks, such as semantic segmentation of whole slide images (WSIs).
Artificial Immune Systems (INTROS 2)
The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time.
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm
It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset.
Theoretical formulation and analysis of the deterministic dendritic cell algorithm
Our analysis suggests that the standard dDCA has a runtime complexity of O(n2) for the worst-case scenario, where n is the number of input data instances.
The Dendritic Cell Algorithm for Intrusion Detection
Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.
