Papers with Code 아카이브

Histopathological Image Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

코드

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

https://github.com/EIDOSlab/UNITOPATHO

공식 코드

UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading

https://github.com/aiff22/deepcmorph

공식 코드

Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

https://github.com/alexander-rakhlin/ICIAR2018

공식 코드

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

https://github.com/bghojogh/BUT-BUNCA-Triplet-Sampling

공식 코드

Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem

https://github.com/bghojogh/Fisher-Triplet-Contrastive-Loss

공식 코드

Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

https://github.com/bghojogh/Histopathology-Magnification-Generalization

공식 코드

Magnification Generalization for Histopathology Image Embedding

https://github.com/bghojogh/Offline-Online-Triplet-Mining

공식 코드

Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches

https://github.com/bghojogh/Siamese-Network-Histopathology

공식 코드

Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

https://github.com/bzhanguru/paper_class-m

공식 코드

CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification

https://github.com/gokberkgul/self-learning-transformer-mil

공식 코드

Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels

https://github.com/jamesdolezal/slideflow

공식 코드

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

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

공식 코드

DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images

https://github.com/medgift/iMIMIC-RCVs

공식 코드

Regression Concept Vectors for Bidirectional Explanations in Histopathology

https://github.com/michalkoziarski/DiagSet

공식 코드

DiagSet: a dataset for prostate cancer histopathological image classification

https://github.com/sacmehta/HATNet

공식 코드

HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images

https://github.com/sigma10010/histo_img_cls

공식 코드

Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings

https://github.com/srinidhiPY/SSL_CR_Histo

공식 코드

Self-supervised driven consistency training for annotation efficient histopathology image analysis

https://github.com/ssen7/histo_cap_transformers

공식 코드

Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT

https://github.com/stegmuel/ScoreNet

공식 코드

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

https://github.com/xiely-123/shisrcnet

공식 코드

SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.