Papers with Code 아카이브

Cancer Classification

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보존된 평가 기록

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코드

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

https://github.com/undercutspiky/sfl

공식 코드

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

https://github.com/wangshansong1/Triad

공식 코드

Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging

https://github.com/wangxb96/eode

공식 코드

Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner

https://github.com/xjtuai/histogym

공식 코드

HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis

https://github.com/yhygao/Efficient_Data_Augmentation

공식 코드

Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

https://github.com/yifeizhangcs/visual-attention-prompt

공식 코드

Visual Attention Prompted Prediction and Learning

https://github.com/zara77/t-cell-receptor-protein-sequences-and-sparse-coding

공식 코드

T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification

https://github.com/zhangxiaoyu11/OmiVAE

공식 코드

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/zhangxiaoyu11/XOmiVAE

공식 코드

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

https://github.com/Chanakya-Ekbote/GANs_and_VAC_for_RNA-Sequences

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix

Skin Lesion Synthesis with Generative Adversarial Networks

https://github.com/NadiaBlostein/Ai4GoodLab_CombatCervicalCancer

CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images

https://github.com/Noodles-321/OralScreen

A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

https://github.com/aseslamian/BreastCancerTransferModels

Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

https://github.com/avakanski/Graph_NNs_for_Multi_Omics_Integration

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

https://github.com/avakanski/LASSO_MOGAT

LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification

https://github.com/cekbote/gans_and_vaes_for_rna-seq

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/elliewith/XOmiVAE

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

https://github.com/ericwu09/mammo-cigan

Conditional Infilling GANs for Data Augmentation in Mammogram Classification

https://github.com/lovelyscientist/rna-seq-vae

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/manhcuongk55/gender-classification-by-hand

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

https://github.com/richardobi/medigan

Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data

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