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Found 9 papers, 1 papers with code
Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection.
Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring
The outlier score derived from the OOD detector is used to modulate sample selection for the subsequent semi-SL stage, ensuring that samples conforming to the distribution of the few labeled samples are more frequently exposed to the subsequent semi-SL framework.
Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers
Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images.
Ranked #1 on
Image Classification
on BreakHis
Breast Cancer Histology Image Classification
Image Classification
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade.
EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning
With our pipeline, we achieved an average area under the curve (AUC) of 0. 964 for tumor detection, and 0. 942 for histological subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA dataset.
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation
Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work.
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network
We present WSSAMNet, a weakly supervised method for medical image registration.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different.
