Recognition: unknown
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
read the original abstract
This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin. The challenge was hosted in 2018 at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Granada, Spain. The dataset included over 12,500 images across 3 tasks. 900 users registered for data download, 115 submitted to the lesion segmentation task, 25 submitted to the lesion attribute detection task, and 159 submitted to the disease classification task. Novel evaluation protocols were established, including a new test for segmentation algorithm performance, and a test for algorithm ability to generalize. Results show that top segmentation algorithms still fail on over 10% of images on average, and algorithms with equal performance on test data can have different abilities to generalize. This is an important consideration for agencies regulating the growing set of machine learning tools in the healthcare domain, and sets a new standard for future public challenges in healthcare.
This paper has not been read by Pith yet.
Forward citations
Cited by 21 Pith papers
-
MedCore: Boundary-Preserving Medical Core Pruning for MedSAM
MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.
-
Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning
LoRA adapters fix collapsed visual CLS token attention in CLIP for superior cross-domain few-shot learning, and the new Semantic Probe framework revives prompt methods to reach state-of-the-art on four benchmarks.
-
XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity
XTinyU-Net identifies the smallest stable U-Net configuration using a Jacobian sensitivity metric on unlabeled images at initialization, achieving comparable accuracy to full models with 400-1600 times fewer parameters.
-
Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
PriUS enforces uncertainty estimates in segmentation models via evidential learning to match image contrast, corruption levels, and shape complexity, yielding more consistent uncertainty on ACDC, ISIC, and WHS dataset...
-
Towards Fine-Grained and Verifiable Concept Bottleneck Models
A verifiable CBM framework grounds concepts in localized image patches, achieving comparable accuracy to standard CBMs on medical benchmarks while enabling direct inspection of concept correctness.
-
Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis
KNT applies key-conditioned nonlinear obfuscation to split-inference features, cutting re-identification AUC from 0.635 to 0.586 with 0.15 ms overhead and under 1 pp accuracy loss.
-
Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation
GeoProto enriches appearance prototypes with geometric offsets from an ordinal shape branch to improve cross-domain few-shot medical image segmentation.
-
XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity
A Jacobian sensitivity curve computed at initialization identifies the narrowest U-Net configuration that avoids performance collapse, matching nnU-Net accuracy with 400-1600x fewer parameters on six medical datasets.
-
Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective
TaCo contrastively embeds semantic, generative, and transformation tasks from medical imaging into a joint space to reveal which tasks cluster, blend, or remain distinct.
-
RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
-
Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection
A single model trained on one normal sample per dataset from nine heterogeneous medical sources achieves state-of-the-art anomaly detection in one-shot universal, full-shot universal, one-shot specialized, and full-sh...
-
SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
SemiGDA aligns feature and semantic distributions via dual encoders and skip adapters to boost semi-supervised medical image segmentation.
-
MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
-
Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement
A new Mean Shift Density Enhancement procedure applied to self-supervised embeddings yields state-of-the-art anomaly detection AUC and average precision on seven medical imaging datasets.
-
Bias-constrained multimodal intelligence for equitable and reliable clinical AI
BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance unde...
-
Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI
Risk-Calibrated Learning reduces critical error rates in medical AI by 20-92% across four imaging datasets by embedding a severity matrix into the optimization.
-
M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
M-IDoL learns modality-specific and diverse representations by maximizing inter-modality entropy and minimizing intra-modality uncertainty through information decomposition in MoE subspaces.
-
Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation
Med-DisSeg uses a dispersive loss on batch representations plus adaptive multi-scale decoding to achieve state-of-the-art fine-grained segmentation on five medical imaging datasets.
-
Improving Model Safety by Targeted Error Correction
A dual GBDT error classifier reduces dangerous misclassifications by 12-34% on medical and animal image datasets with under 2% added latency.
-
DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets
DMDSC adapts simplex-classifier margins dynamically according to label frequency to tighten clustering on rare medical classes and improve open-set rejection on imbalanced imaging datasets.
-
Attention Is not Everything: Efficient Alternatives for Vision
A survey that taxonomizes non-Transformer vision models and evaluates their practical trade-offs across efficiency, scalability, and robustness.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.