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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/pranavsinghps1/CASS

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CASS: Cross Architectural Self-Supervision for Medical Image Analysis

https://github.com/psosnin/AbstractGradientTraining

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Certification for Differentially Private Prediction in Gradient-Based Training

https://github.com/pyjulie/monica

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MONICA: Benchmarking on Long-tailed Medical Image Classification

https://github.com/pyjulie/ussl4mic

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Universal Semi-Supervised Learning for Medical Image Classification

https://github.com/raghavian/loTeNet_pytorch

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Tensor Networks for Medical Image Classification

https://github.com/rwightman/pytorch-image-models

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ResNet strikes back: An improved training procedure in timm

https://github.com/sadimanna/skid

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SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

https://github.com/sadimanna/sslm

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SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

https://github.com/sajjad-sh33/medical-transfer-learning

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Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models

https://github.com/scott-yjyang/diffmic

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DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification

https://github.com/sdoerrich97/rethinking-model-prototyping-medmnistplus

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Rethinking model prototyping through the MedMNIST+ dataset collection

https://github.com/senyuan-juncheng/TensorProjectionLayer

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TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks

https://github.com/siomvas/ARIA

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ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification

https://github.com/siyuanyan1/pldg

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Prompt-driven Latent Domain Generalization for Medical Image Classification

https://github.com/skyz215/proco

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ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification

https://github.com/stanfordmlgroup/MedSelect

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MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning

https://github.com/sun-umn/Transfer-Learning-in-Medical-Imaging

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Rethinking Transfer Learning for Medical Image Classification

https://github.com/sun-umn/ttl

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Rethinking Transfer Learning for Medical Image Classification

https://github.com/sunyoung98/Brain_Tumor_Detection_XAI

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Explainability of Deep Neural Networks for Brain Tumor Detection

https://github.com/sushmitasarker/sgc_for_medical_image_classification

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Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?

https://github.com/szbonaldo/fedmlp

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FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity

https://github.com/tayebiarasteh/dp_cxr

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Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

https://github.com/tayebiarasteh/fldomain

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Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models

https://github.com/tayebiarasteh/privacydomain

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Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy

https://github.com/tayebiarasteh/vit-med

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Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images

https://github.com/tdlhl/LoRKD

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LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/thuwuyinjun/Chef

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CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)

https://github.com/tifat58/irr-with-cbm-rag

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Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG

https://github.com/tobarc/privacy-aware-image-classification-with-knn

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Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification

https://github.com/tufts-ml/ssl-vs-ssl-benchmark

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Systematic comparison of semi-supervised and self-supervised learning for medical image classification

https://github.com/ubc-tea/dne-foundation-model-fairness

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Debiased Noise Editing on Foundation Models for Fair Medical Image Classification

https://github.com/ubc-tea/fcro-fair-classification-orthogonal-representation

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On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

https://github.com/um-dsp/PRICURE

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PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting

https://github.com/vcheplygina/cats-scans

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Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification

https://github.com/vengdeng/fcro

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On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

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