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Medical Image Classification

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하위 태스크: 1
평가표: 11
보존된 평가 기록: 20

Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/kalelpark/FG-SSL

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Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification

https://github.com/kalelpark/ral

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Robust Asymmetric Loss for Multi-Label Long-Tailed Learning

https://github.com/kangningthu/its2clr

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Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning

https://github.com/karryren/uml

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Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

https://github.com/kiananvari/mtms-a-domain-adaptive-meta-knowledge-distillation-model-for-medical-image-classification

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Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

https://github.com/kqp1227/sensitive-channel-pruning

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Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis

https://github.com/liuquande/FedIRM

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Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

https://github.com/liuzhuang13/DenseNet

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Densely Connected Convolutional Networks

https://github.com/llyxc/ada-abc

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Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council

https://github.com/lucasdedieu/noiseresilienthistopathology

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Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification

https://github.com/m-nauta/pipnet

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Interpreting and Correcting Medical Image Classification with PIP-Net

https://github.com/matanat/dae_counterfactual

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Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder

https://github.com/maxium0526/ptrn

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Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification

https://github.com/mayurmallya/deepguide

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Deep Multimodal Guidance for Medical Image Classification

https://github.com/med-air/harmofl

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HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

https://github.com/med-air/ttadc

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Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

https://github.com/mediabrain-sjtu/lorkd

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LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models

https://github.com/melanibe/failure_detection_benchmark

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Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed

https://github.com/mevalle/v-nets

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V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network Models

https://github.com/microsoft/hi-ml/tree/main/hi-ml-multimodal

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Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing

https://github.com/mobarakol/robustness_stress_testing

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Robustness Stress Testing in Medical Image Classification

https://github.com/mohofar/beta-rank

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Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis

https://github.com/mrsaraei/vit-2spn

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ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification

https://github.com/nayeemrizve/ups

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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

https://github.com/neugmd/blrm

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Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification

https://github.com/nourhanb/biaspruner

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BiasPruner: Debiased Continual Learning for Medical Image Classification

https://github.com/noviceman-prog/focalconvnet

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Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network

https://github.com/nus-tim/cect

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CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image Classification

https://github.com/obiyoag/crl

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Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification

https://github.com/omarsherif200/hdkd

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HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classification

https://github.com/openmedlab/MedFM

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MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification

https://github.com/ortonwang/pcdal

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PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

https://github.com/payalkamboj/cupkl

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Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM

https://github.com/peterhan91/diffchest

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Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining

https://github.com/peterlipan/lmd

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Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration

https://github.com/philip-mueller/lovt

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Joint Learning of Localized Representations from Medical Images and Reports

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