Medical Image Classification Task

Medical image classification aims to automatically categorize medical images (e.g., X-rays, CT scans) into predefined classes, assisting in diagnosis and treatment planning. Current research emphasizes addressing challenges like limited data, class imbalance, and the need for explainable AI, leading to exploration of various model architectures including vision transformers, convolutional neural networks, and ensemble methods like gradient boosting decision trees, often enhanced by techniques such as prompt tuning, self-supervised pre-training, and knowledge distillation. These advancements hold significant potential for improving diagnostic accuracy, efficiency, and reliability in healthcare, ultimately impacting patient care and clinical decision-making.

Papers

September 25, 2024

Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data
A. Yarkın Yıldız, Asli Kalayci
Tabular Data Medical Diagnosis Ensemble Method Gradient Boosted Decision Tree Medical Image Classification Task Gradient Boosting Decision Tree

August 11, 2024

PRECISe : Prototype-Reservation for Explainable Classification under Imbalanced and Scarce-Data Settings
Vaibhav Ganatra, Drishti Goel
Deep Learning Model Medical Image Datasets Visual Prototype Explainable Classification Group Imbalance Interpretable by Design Scarce Data Interpretable Prediction Medical Image Classification Task

July 1, 2024

Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images
Wenqiang Zu, Shenghao Xie, Qing Zhao, Guoqi Li, Lei Ma
Fine Tuning Medical Image Parameter Efficient Fine Tuning Prompt Tuning Model Calibration Medical Image Classification Task

June 24, 2024

Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks
Zihao Jin, Yingying Fang, Jiahao Huang, Caiwen Xu, Simon Walsh, Guang Yang
Vision Transformer Medical Image Classification Transformer Network Chest CT Medical Image Classification Task Sequence Diffusion

March 6, 2024

MedMamba: Vision Mamba for Medical Image Classification
Yubiao Yue, Zhenzhang Li
Convolutional Neural Network Medical Image Medical Image Classification Vision Mamba Medical Image Classification Task

November 24, 2023

ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni
Supervised ImageNet New Initialization Architecture Perspective Normalization Layer Federated Image Classification Medical Image Classification Task Diva 360

October 30, 2023

Are Natural Domain Foundation Models Useful for Medical Image Classification?
Joana Palés Huix, Adithya Raju Ganeshan, Johan Fredin Haslum, Magnus Söderberg, Christos Matsoukas, Kevin Smith
Deep Learning Foundation Model Supervised ImageNet Medical Image Classification Medical Image Datasets Medical Image Classification Task Domain Specific Foundation Model

September 19, 2023

Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI
Gianluca Carloni, Eva Pachetti, Sara Colantonio
LeArning Abstract Medical Image Magnetic Resonance Imaging Prostate Cancer Event Causality Identification Medical Image Classification Task Visual Causality Discovery

August 12, 2023

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
Deep Learning Large Scale Medical Imaging Masked Autoencoders Self Supervised Pre Training Small Datasets Medical Image Classification Task Self Supervised Pre Training Method

July 12, 2023

Learning from Exemplary Explanations
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
LeArning Abstract Model Explanation Quality Explanation Interactive Machine Learning Explainability Problem Medical Image Classification Task Explanation Based

June 26, 2023

FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
Faris Almalik, Naif Alkhunaizi, Ibrahim Almakky, Karthik Nandakumar
Vision Transformer Split Learning Medical Image Classification Task Federated Split Learning

June 1, 2023

Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification
Cara Van Uden, Christian Bluethgen, Maayane Attias, Malgorzata Polacin, Haiwei Henry Guo, Neha Simha, Rishi Raj, Curtis Langlotz
Adaptive Importance Medical Image Classification Task Idiopathic Pulmonary Fibrosis Interstitial Lung Disease

March 2, 2023

Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida
Domain Adaptation Cluster Structure Semi Supervised Domain Adaptation Imbalanced Medical Image Medical Image Classification Task Class Imbalanced

October 15, 2022

RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Ajay Jaiswal, Kumar Ashutosh, Justin F Rousseau, Yifan Peng, Zhangyang Wang, Ying Ding
Knowledge Distillation Medical Image Classification Task Direct Roi Prediction Noisy MRI

November 20, 2021

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Margarita L. Zuley, Shandong Wu
Deep Learning Deep Learning Model Image Classification One Class Classification Imbalanced Medical Image Medical Image Classification Task

Medical Image Classification Task

Papers

Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data

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

Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images

Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks

MedMamba: Vision Mamba for Medical Image Classification

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

Are Natural Domain Foundation Models Useful for Medical Image Classification?

Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Learning from Exemplary Explanations

FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling

Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification

Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification