2 research outputs found
M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation
Polyp segmentation has recently garnered significant attention, and multiple
methods have been formulated to achieve commendable outcomes. However, these
techniques often confront difficulty when working with the complex polyp
foreground and their surrounding regions because of the nature of convolution
operation. Besides, most existing methods forget to exploit the potential
information from multiple decoder stages. To address this challenge, we suggest
combining MetaFormer, introduced as a baseline for integrating CNN and
Transformer, with UNet framework and incorporating our Multi-scale Upsampling
block (MU). This simple module makes it possible to combine multi-level
information by exploring multiple receptive field paths of the shallow decoder
stage and then adding with the higher stage to aggregate better feature
representation, which is essential in medical image segmentation. Taken all
together, we propose MetaFormer Multi-scale Upsampling Network (MUNet) for
the polyp segmentation task. Extensive experiments on five benchmark datasets
demonstrate that our method achieved competitive performance compared with
several previous methods
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical
imaging exams, are "long-tailed" \unicode{x2013} there are a few common
findings followed by many more relatively rare conditions. In chest
radiography, diagnosis is both a long-tailed and multi-label problem, as
patients often present with multiple findings simultaneously. While researchers
have begun to study the problem of long-tailed learning in medical image
recognition, few have studied the interaction of label imbalance and label
co-occurrence posed by long-tailed, multi-label disease classification. To
engage with the research community on this emerging topic, we conducted an open
challenge, CXR-LT, on long-tailed, multi-label thorax disease classification
from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset
of over 350,000 CXRs, each labeled with at least one of 26 clinical findings
following a long-tailed distribution. We synthesize common themes of
top-performing solutions, providing practical recommendations for long-tailed,
multi-label medical image classification. Finally, we use these insights to
propose a path forward involving vision-language foundation models for few- and
zero-shot disease classification