Domain-stratified Training for Cross-organ and Cross-scanner Adenocarcinoma Segmentation in the COSAS 2024 Challenge
This manuscript presents an image segmentation algorithm developed for the Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation (COSAS 2024) challenge. We adopted an organ-stratified and scanner-stratified approach to train multiple Upernet-based segmentation models and subsequently ensembled the results. Despite the challenges posed by the varying tumor characteristics across different organs and the differing imaging conditions of various scanners, our method achieved a final test score of 0.7643 for Task 1 and 0.8354 for Task 2. These results demonstrate the adaptability and efficacy of our approach across diverse conditions. Our model's ability to generalize across various datasets underscores its potential for real-world applications.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationSegmentationSemantic SegmentationTask 2Similar Papers 제목 키워드 기반
Cross-position Activity Recognition with Stratified Transfer Learning
Human activity recognition aims to recognize the activities of daily living by utilizing the sensors on different body parts. However, when the labeled data from a certain body position (i.e. target domain) is missing, h…
Activity RecognitionHuman Activity RecognitionPositionTransfer LearningStratified Knowledge-Density Super-Network for Scalable Vision Transformers
Training and deploying multiple vision transformer (ViT) models for different resource constraints is costly and inefficient. To address this, we propose transforming a pre-trained ViT into a stratified knowledge-density…
Model CompressionKACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning
Context engineering can improve large language models without updating their weights, but mathematical reasoning exposes a key limitation: feedback accumulated in one growing prompt causes context bloat and limits the am…
Mathematical ReasoningArabicDialectHub: A Cross-Dialectal Arabic Learning Resource and Platform
We present ArabicDialectHub, a cross-dialectal Arabic learning resource comprising 552 phrases across six varieties (Moroccan Darija, Lebanese, Syrian, Emirati, Saudi, and MSA) and an interactive web platform. Phrases we…
Distractor GenerationOrgan at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods us…
AnatomyNeural Architecture SearchSegmentation