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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/bionlplab/longitudinal_transformer_for_survival_analysis

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Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

https://github.com/cheliu-computation/m-flag-miccai2023

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M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization

https://github.com/chrismats/medical_transformers

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Pretrained ViTs Yield Versatile Representations For Medical Images

https://github.com/cobypenso/noise-aware-conformal-prediction

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Estimating the Conformal Prediction Threshold from Noisy Labels

https://github.com/compai-lab/2024-miccai-grail-kiechle

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Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?

https://github.com/cristianopatricio/explainable-deep-learning-methods-in-medical-image-classification-a-survey

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Explainable Deep Learning Methods in Medical Image Classification: A Survey

https://github.com/csiro-robotics/l3dmc

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L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space

https://github.com/cvlab-stonybrook/SelfMedMAE

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Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

https://github.com/declanmcintosh/legallcuda

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Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification

https://github.com/dial-rpi/regression-metric-loss

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Regression Metric Loss: Learning a Semantic Representation Space for Medical Images

https://github.com/dingmyu/davit

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DaViT: Dual Attention Vision Transformers

https://github.com/dongkyuk/CheXFusion

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CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification

https://github.com/doviledo/transferability-in-medical-imaging

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On dataset transferability in medical image classification

https://github.com/dzp2095/fedlsm

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Scale Federated Learning for Label Set Mismatch in Medical Image Classification

https://github.com/fallingnight/pspg

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Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification

https://github.com/faresmalik/sevit

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Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification

https://github.com/fengbeiliu/semi-chest

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Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification

https://github.com/frankkramer-lab/ensmic

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An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks

https://github.com/function2-llx/pumit

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Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks

https://github.com/ganatra-v/precise

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PRECISe : Prototype-Reservation for Explainable Classification under Imbalanced and Scarce-Data Settings

https://github.com/geccoproject/gecco

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A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification

https://github.com/guanghuifu/medical_dino_eval

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Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification

https://github.com/hamish-haggerty/base_rbt

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Self-supervised learning for skin cancer diagnosis with limited training data

https://github.com/haqishen/SIIM-ISIC-Melanoma-Classification-1st-Place-Solution

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Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge

https://github.com/hasibzunair/synthetic-covid-cxr-dataset

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Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation

https://github.com/hathawayxxh/CRCKD

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Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification

https://github.com/huaxiuyao/mlti

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Meta-Learning with Fewer Tasks through Task Interpolation

https://github.com/huoxiangzuo/HiFuse

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HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification

https://github.com/imankhazrak/DDPM_X-Ray

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Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling

https://github.com/iml-dkfz/sf-visuals

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Understanding Silent Failures in Medical Image Classification

https://github.com/ipmi-icns-uke/sparsam

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Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per class

https://github.com/jiarunliu/co-correcting

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Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction

https://github.com/joanaapa/foundation-medical

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Are Natural Domain Foundation Models Useful for Medical Image Classification?

https://github.com/jwicaksana/fca

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FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring

https://github.com/jxliu-ai/kpl

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KPL: Training-Free Medical Knowledge Mining of Vision-Language Models

https://github.com/kaczmarj/champkit

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Evaluating histopathology transfer learning with ChampKit

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