Multi-head Attention-based Deep Multiple Instance Learning
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Updated
Mar 5, 2024 - Python
Multi-head Attention-based Deep Multiple Instance Learning
Statistical classifier for diagnosing ovarian cancer from immune repertoires
CLI tool to run specimen-level inference on whole slide images
Detecting Severe Malaria Anaemia and investigating the morphological characteristics of red blood cells at its presenc
[MICCAI'22] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.
Implementation of LA_MIL, Local Attention Graph-based Transformer for WSIs, PyTorch
Fungal classification at 10x resolution
Experiments testing ordinal and multiple instance learning neural networks
Code for the paper "Contrastive Pre-Training and Multiple Instance Learning for Predicting Tumor Microsatellite Instability" (EMBC 2024)
This repo for the paper titled "SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification"
Official Pytorch Code of Our Paper: Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need
Employing Molecular Conformations for Ligand-based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning
Code for the paper "Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification".
Attention Based Multi-Instance Thyroid Cytopathological Diagnosis with Multi-Scale Feature Fusion
[IEEE TMI 2024] Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
MilGNet: Deep Multiple Instance Learning on Heterogeneous Graph for Drug-disease Association Prediction
Repository for Hierarchical Attention-Guided Multiple Instance Learning
PyTorch implementation of MS-DA-MIL
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