Histopathological gastric cancer detection on GasHisSDB dataset using deep ensemble learning
Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the
need for early detection to improve patient survival rates. The current clinical gold standard …
need for early detection to improve patient survival rates. The current clinical gold standard …
Anatomical context protects deep learning from adversarial perturbations in medical imaging
Deep learning has achieved impressive performance across a variety of tasks, including
medical image processing. However, recent research has shown that deep neural networks …
medical image processing. However, recent research has shown that deep neural networks …
Residual attention based network for hand bone age assessment
Computerized automatic methods have been employed to boost the productivity as well as
objectiveness of hand bone age assessment. These approaches make predictions …
objectiveness of hand bone age assessment. These approaches make predictions …
Identify representative samples by conditional random field of cancer histology images
Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment
plan as well. To detect abnormality in histopathology images with prevailing patch-based …
plan as well. To detect abnormality in histopathology images with prevailing patch-based …
Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search
K Hu, B Shen, Y Zhang, C Cao, F Xiao, X Gao - Neurocomputing, 2019 - Elsevier
Accurate quantitative analysis of the retinal layer in optical coherence tomography (OCT)
images plays a crucial role in detecting and diagnosing ocular diseases. In this paper, we …
images plays a crucial role in detecting and diagnosing ocular diseases. In this paper, we …
Automatic multi-tissue segmentation in pancreatic pathological images with selected multi-scale attention network
E Gao, H Jiang, Z Zhou, C Yang, M Chen, W Zhu… - Computers in Biology …, 2022 - Elsevier
The morphology of tissues in pathological images has been used routinely by pathologists
to assess the degree of malignancy of pancreatic ductal adenocarcinoma (PDAC) …
to assess the degree of malignancy of pancreatic ductal adenocarcinoma (PDAC) …
A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation
Background and objective Prostate segmentation on Magnetic Resonance (MR) imaging is
problematic because disease changes the shape and boundaries of the gland and it can be …
problematic because disease changes the shape and boundaries of the gland and it can be …
Negative pseudo labeling using class proportion for semantic segmentation in pathology
H Tokunaga, BK Iwana, Y Teramoto… - Computer Vision–ECCV …, 2020 - Springer
In pathological diagnosis, since the proportion of the adenocarcinoma subtypes is related to
the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for …
the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for …
[HTML][HTML] Learning binary and sparse permutation-invariant representations for fast and memory efficient whole slide image search
Considering their gigapixel sizes, the representation of whole slide images (WSIs) for
classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance …
classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance …
CNN and deep sets for end-to-end whole slide image representation learning
Digital pathology has enabled us to capture, store and analyze scanned biopsy samples as
digital images. Recent advances in deep learning are contributing to computational …
digital images. Recent advances in deep learning are contributing to computational …